The purpose of this project is to verify the quality of the training algorithms in R packages that provide neural network of perceptron type (one input layer, one normalized layer, one hidden layer with nonlinear activation function usually tanh(), one normalized layer, one output output layer) for regression purpose i.e. \_(NN(X1, ..., Xn) = E[Y]\_).\nMachineShop monmlp neural neuralnet NeuralNetTools NeuralSens\nNlinTS nnet nnetpredint nnfor onnx OptimClassifier\nOSTSC pnn polyreg predictoR qrnn QuantumOps\nquarrint radiant.model rasclass rcane regressoR rminer\nrnn RSNNS ruta simpleNeural snnR softmaxreg\nSojourn.Data spnn TeachNet tensorflow tfestimators trackdem\nTrafficBDE validann zFactor\nmDette 500 3 5 26\nmFriedman 500 5 5 36\nmIshigami 500 3 10 51\nmRef153 153 5 3 22\nuDmod1 51 1 6 19\nuDmod2 51 1 5 16\nuDreyfus1 51 1 3 10\nuDreyfus2 51 1 3 10\nuGauss1 250 1 5 16\nuGauss2 250 1 4 13\nuGauss3 250 1 4 13\nuNeuroOne 51 1 2 7\nFollowing are the results of each package per dataset.\nmDette_AMORE::train_ADAPTgdwm_02 2.281 0.3743 0.2872\niter=10000 lr=0.009 momentum=0.8 sigmoid AMORE\nmDette_ANN2::neuralnetwork_sgd_09 0.999 0.3886 0.2954\niter=4000 lr=0.01 Require fine tuning ANN2\nmDette_brnn::brnn_gaussNewton_04 0.141 0.2639 0.2096 *** Different NN brnn\nmDette_deepnet::gradientdescent_03 0.750 0.4301 0.3233 numepochs=1000 Bad on univariate datasets deepnet\nmDette_h2o::h2o.deeplearning_06 0.515 2.3295 1.6713 maxiter=200 Bad on univariate datasets h2o\nmDette_MachineShop::fit.NNetModel_04 0.060 0.4618 0.3469 maxiter = 150 uses nnet MachineShop\nmDette_minpacklm::nlsLM_02 0.284 0.1043 0.0832 ** maxiter = 150 Require hand-made formulas and scaling minpacklm\nmDette_momlp::monmlp.fit_BFGS_07 0.266 0.3014 0.2372 maxiter = 300 uses optimx momlp\nmDette_neural::mlptrain_01 56.402 5.9735 4.4623 maxiter = 1000 classification neural\nmDette_neuralnet::neuralnet_slr_03 12.830 0.4553 0.3442 neuralnet\nmDette_nlsr::nlxb_05 0.359 0.1665 0.1335 ** maxiter = 150 Require hand-made formulas and scaling nlsr\nmDette_nnet::nnet_01 0.063 0.3567 0.2736 maxiter = 150 uses optim BFGS nnet\nmDette_qrnn::qrnn.fit_02 0.404 0.0837 0.0579 maxiter = 700 qrnn\nmDette_radiantmodel::radiantmodel_06 1.653 0.0703 0.0556 maxiter=10000 preset uses nnet radiantmodel\nmDette_rcane::rlm_01 0.000 14.0137 11.3889 maxiter = 1000 linear only rcane\nmDette_rminer::fit_01 0.167 0.3595 0.2771 maxiter = 150 rminer\nmDette_RSNNS::mlp_SCG_06 1.186 0.4080 0.3029 maxiter = 1000 RSNNS\nmDette_simpleNeural::sN.MLPtrain_01 0.000 14.0137 11.3889 maxiter = 1000 for bi- or multiclass classification simpleNeural\nmDette_snnR::snnR_02 0.062 0.8399 0.6390 maxiter = 200 better on multi snnR\nmDette_softmaxreg::softmaxreg_01 0.016 8.1656 6.5262 maxiter = 1000 softmaxreg\nmDette_tsensembler::tsensembler_01 1.676 0.1726 0.1157 maxiter = 150 uses nnet tsensembler\nmDette_validann::ann_BFGS_07 1.689 0.2947 0.2241 maxiter = 200 optim validann\nmFriedman_AMORE::train_ADAPTgd_08 0.562 0.0267 0.0214\niter=3500 lr=0.01 Require scaling AMORE\nmFriedman_ANN2::neuralnetwork_sgd_04 1.034 0.0162 0.0129\nmFriedman_brnn::brnn_gaussNewton_02 0.203 0.0052 0.0042 *** Different NN brnn\nmFriedman_deepnet::gradientdescent_05 0.796 0.0309 0.0242 numepochs=1000 Bad on univariate datasets deepnet\nmFriedman_h2o::h2o.deeplearning_09 0.657 0.0327 0.0258 maxiter=200 Bad on univariate datasets h2o\nmFriedman_MachineShop::fit.NNetModel_01 0.121 0.0090 0.0071 maxiter = 150 uses nnet MachineShop\nmFriedman_minpacklm::nlsLM_03 0.453 0.0051 0.0041 ** maxiter = 150 Require hand-made formulas and scaling minpacklm\nmFriedman_momlp::monmlp.fit_BFGS_05 0.338 0.0094 0.0076 maxiter = 300 uses optimx momlp\nmFriedman_neural::mlptrain_01 69.804 0.1812 0.1344 maxiter = 1000 classification neural\nmFriedman_neuralnet::neuralnet_rprop-_04 12.065 0.0689 0.0490 Erratic on multivariate datasets neuralnet\nmFriedman_nlsr::nlxb_02 0.625 0.0047 0.0038 ** maxiter = 150 Require hand-made formulas and scaling nlsr\nmFriedman_nnet::nnet_06 0.076 0.0127 0.0102 maxiter = 150 uses optim BFGS nnet\nmFriedman_qrnn::qrnn.fit_08 0.426 0.0051 0.0029 maxiter = 700 qrnn\nmFriedman_radiantmodel::radiantmodel_02 0.941 0.0041 0.0033 maxiter=10000 preset uses nnet radiantmodel\nmFriedman_rcane::rlm_01 0.000 0.6122 0.5654 maxiter = 1000 linear only rcane\nmFriedman_rminer::fit_03 0.216 0.0094 0.0074 maxiter = 150 rminer\nmFriedman_RSNNS::mlp_SCG_07 1.163 0.0188 0.0147 maxiter = 1000 RSNNS\nmFriedman_simpleNeural::sN.MLPtrain_01 0.001 0.6122 0.5654 maxiter = 1000 for bi- or multiclass classification simpleNeural\nmFriedman_snnR::snnR_02 0.255 0.0457 0.0357 maxiter = 200 better on multi snnR\nmFriedman_softmaxreg::softmaxreg_01 0.000 0.2348 0.1880 maxiter = 1000 softmaxreg\nmFriedman_tsensembler::tsensembler_07 1.757 0.0038 0.0028 maxiter = 150 uses nnet tsensembler\nmFriedman_validann::ann_BFGS_10 2.195 0.0114 0.0088 maxiter = 200 optim validann\nmIshigami_AMORE::train_ADAPTgd_06 0.984 0.7192 0.5349\nmIshigami_ANN2::neuralnetwork_sgd_04 1.568 0.6500 0.5050\nmIshigami_brnn::brnn_gaussNewton_04 0.328 0.6583 0.5062 *** Different NN brnn\nmIshigami_deepnet::gradientdescent_07 0.937 0.8201 0.6072 numepochs=1000 Bad on univariate datasets deepnet\nmIshigami_h2o::h2o.deeplearning_10 0.734 1.0089 0.7433 maxiter=200 Bad on univariate datasets h2o\nmIshigami_MachineShop::fit.NNetModel_08 0.324 0.6091 0.4546 maxiter = 150 uses nnet MachineShop\nmIshigami_minpacklm::nlsLM_04 1.062 0.6682 0.5170 ** maxiter = 150 Require hand-made formulas and scaling minpacklm\nmIshigami_momlp::monmlp.fit_BFGS_08 0.427 0.6225 0.4769 maxiter = 300 uses optimx momlp\nmIshigami_neural::mlptrain_05 103.094 2.8352 1.9905 maxiter = 1000 classification neural\nmIshigami_neuralnet::neuralnet_rprop+_01 28.040 3.6898 2.9776 Erratic on difficult datasets neuralnet\nmIshigami_nlsr::nlxb_06 1.172 0.5895 0.4575 ** maxiter = 150 Require hand-made formulas and scaling nlsr\nmIshigami_nnet::nnet_01 0.134 0.7194 0.5373 maxiter = 150 uses optim BFGS nnet\nmIshigami_qrnn::qrnn.fit_10 1.070 0.5622 0.3440 maxiter = 700 qrnn\nmIshigami_radiantmodel::radiantmodel_01 0.670 0.4761 0.3500 maxiter=10000 preset uses nnet radiantmodel\nmIshigami_rcane::rlm_01 0.001 4.9597 3.9131 maxiter = 1000 linear only rcane\nmIshigami_rminer::fit_03 0.464 0.6662 0.4870 maxiter = 150 rminer\nmIshigami_RSNNS::mlp_SCG_06 1.480 0.6930 0.5113 maxiter = 1000 RSNNS\nmIshigami_simpleNeural::sN.MLPtrain_01 0.003 4.9597 3.9131 maxiter = 1000 for bi- or multiclass classification simpleNeural\nmIshigami_snnR::snnR_04 0.263 0.7757 0.5560 maxiter = 200 better on multi snnR\nmIshigami_softmaxreg::softmaxreg_01 0.000 3.6898 2.9776 maxiter = 1000 softmaxreg\nmIshigami_tsensembler::tsensembler_03 1.686 0.2245 0.1666 maxiter = 150 uses nnet tsensembler\nmIshigami_validann::ann_BFGS_01 4.642 0.5424 0.3978 maxiter = 200 optim validann\nuDmod1_nlsr::nlxb_01 15:36:49.791 15:36:49.869 0.078 0.0439 0.0356 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_nlsr::nlxb_02 15:36:49.869 15:36:49.963 0.094 0.0430 0.0344 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_nlsr::nlxb_03 15:36:49.963 15:36:50.041 0.078 0.0433 0.0349 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_nlsr::nlxb_04 15:36:50.041 15:36:50.135 0.094 0.0460 0.0364 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_nlsr::nlxb_05 15:36:50.135 15:36:50.213 0.078 0.0472 0.0375 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_nlsr::nlxb_06 15:36:50.213 15:36:50.291 0.078 0.0950 0.0742 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_nlsr::nlxb_07 15:36:50.307 15:36:50.385 0.078 0.0433 0.0349 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_nlsr::nlxb_08 15:36:50.385 15:36:50.463 0.078 0.0465 0.0390 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_nlsr::nlxb_09 15:36:50.478 15:36:50.556 0.078 0.1012 0.0823 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_nlsr::nlxb_10 15:36:50.556 15:36:50.635 0.079 0.0469 0.0393 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_minpacklm::nlsLM_01 15:46:29.085 15:46:29.147 0.062 0.0433 0.0350 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_minpacklm::nlsLM_02 15:46:29.147 15:46:29.194 0.047 0.5884 0.5069 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_minpacklm::nlsLM_03 15:46:29.194 15:46:29.241 0.047 0.0433 0.0349 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_minpacklm::nlsLM_04 15:46:29.241 15:46:29.303 0.062 0.0418 0.0326 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_minpacklm::nlsLM_05 15:46:29.303 15:46:29.350 0.047 0.0440 0.0355 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_minpacklm::nlsLM_06 15:46:29.350 15:46:29.413 0.063 0.0475 0.0383 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_minpacklm::nlsLM_07 15:46:29.413 15:46:29.460 0.047 0.0797 0.0598 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_minpacklm::nlsLM_08 15:46:29.460 15:46:29.507 0.047 0.5884 0.5069 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_minpacklm::nlsLM_09 15:46:29.507 15:46:29.569 0.062 0.0733 0.0533 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_minpacklm::nlsLM_10 15:46:29.569 15:46:29.616 0.047 0.0433 0.0349 ** maxiter = 150 Require hand-made formulas and scaling\nuDmod1_h2o::h2o.deeplearning_01 15:54:04.748 15:54:05.311 0.563 0.3880 0.3357 maxiter=200 Bad on univariate datasets\nuDmod1_h2o::h2o.deeplearning_02 15:54:05.311 15:54:05.936 0.625 0.2428 0.1958 maxiter=200 Bad on univariate datasets\nuDmod1_h2o::h2o.deeplearning_03 15:54:05.936 15:54:06.483 0.547 0.3503 0.2783 maxiter=200 Bad on univariate datasets\nuDmod1_h2o::h2o.deeplearning_04 15:54:06.483 15:54:06.967 0.484 0.3051 0.2466 maxiter=200 Bad on univariate datasets\nuDmod1_h2o::h2o.deeplearning_05 15:54:06.967 15:54:07.560 0.593 0.3784 0.3125 maxiter=200 Bad on univariate datasets\nuDmod1_h2o::h2o.deeplearning_06 15:54:07.560 15:54:08.060 0.500 0.3151 0.2672 maxiter=200 Bad on univariate datasets\nuDmod1_h2o::h2o.deeplearning_07 15:54:08.060 15:54:08.560 0.500 0.3740 0.3073 maxiter=200 Bad on univariate datasets\nuDmod1_h2o::h2o.deeplearning_08 15:54:08.560 15:54:09.138 0.578 0.3766 0.3139 maxiter=200 Bad on univariate datasets\nuDmod1_h2o::h2o.deeplearning_09 15:54:09.138 15:54:09.716 0.578 0.3696 0.3204 maxiter=200 Bad on univariate datasets\nuDmod1_h2o::h2o.deeplearning_10 15:54:09.716 15:54:10.294 0.578 0.3856 0.3063 maxiter=200 Bad on univariate datasets\nuDmod1_neuralnet::neuralnet_rprop+_01 18:10:38.981 18:10:40.012 1.031 0.0604 0.0458 Erratic on difficult datasets\nuDmod1_neuralnet::neuralnet_rprop+_02 18:10:40.012 18:10:41.699 1.687 0.0615 0.0472 Erratic on difficult datasets\nuDmod1_neuralnet::neuralnet_rprop+_03 18:10:41.699 18:10:42.402 0.703 0.0469 0.0380 Erratic on difficult datasets\nuDmod1_neuralnet::neuralnet_rprop+_04 18:10:42.402 18:10:43.402 1.000 0.0493 0.0395 Erratic on difficult datasets\nuDmod1_neuralnet::neuralnet_rprop+_05 18:10:43.402 18:10:44.261 0.859 0.0616 0.0473 Erratic on difficult datasets\nuDmod1_neuralnet::neuralnet_backprop_01 21:17:55.218 21:18:01.389 6.171 0.5884 0.5069 Fails on difficult datasets\nuDmod1_neuralnet::neuralnet_backprop_02 21:18:01.389 21:18:07.512 6.123 0.5884 0.5069 Fails on difficult datasets\nuDmod1_neuralnet::neuralnet_backprop_03 21:18:07.512 21:18:13.558 6.046 0.5884 0.5069 Fails on difficult datasets\nuDmod1_neuralnet::neuralnet_backprop_04 21:18:13.558 21:18:19.603 6.045 0.5884 0.5069 Fails on difficult datasets\nuDmod1_neuralnet::neuralnet_backprop_05 21:18:19.603 21:18:25.695 6.092 0.5884 0.5069 Fails on difficult datasets\nuDmod1_brnn::brnn_gaussNewton_01 10:21:53.726 10:21:53.742 0.016 0.5885 0.5088 *** Different NN\nuDmod1_brnn::brnn_gaussNewton_02 10:21:53.742 10:21:53.758 0.016 0.5885 0.5088 *** Different NN\nuDmod1_brnn::brnn_gaussNewton_03 10:21:53.758 10:21:53.758 0.000 0.5885 0.5088 *** Different NN\nuDmod1_brnn::brnn_gaussNewton_04 10:21:53.758 10:21:53.773 0.015 0.3340 0.2909 *** Different NN\nuDmod1_brnn::brnn_gaussNewton_05 10:21:53.773 10:21:53.820 0.047 0.0726 0.0559 *** Different NN\nuDmod1_brnn::brnn_gaussNewton_06 10:21:53.820 10:21:53.851 0.031 0.1172 0.0908 *** Different NN\nuDmod1_brnn::brnn_gaussNewton_07 10:21:53.851 10:21:53.851 0.000 0.5885 0.5088 *** Different NN\nuDmod1_brnn::brnn_gaussNewton_08 10:21:53.851 10:21:53.883 0.032 0.0603 0.0449 *** Different NN\nuDmod1_brnn::brnn_gaussNewton_09 10:21:53.883 10:21:53.914 0.031 0.0455 0.0365 *** Different NN\nuDmod1_brnn::brnn_gaussNewton_10 10:21:53.914 10:21:53.945 0.031 0.1164 0.0898 *** Different NN\nuDmod1_AMORE::train_ADAPTgd_01 13:56:35.894 13:56:36.035 0.141 0.1487 0.1147\niter=3500 lr=0.01 Require scaling\nuDmod1_AMORE::train_ADAPTgd_02 13:56:36.035 13:56:36.175 0.140 0.1300 0.1093\nuDmod1_AMORE::train_ADAPTgd_03 13:56:36.175 13:56:36.332 0.157 0.1102 0.0913\nuDmod1_AMORE::train_ADAPTgd_04 13:56:36.332 13:56:36.472 0.140 0.1812 0.1237\nuDmod1_AMORE::train_ADAPTgd_05 13:56:36.472 13:56:36.628 0.156 0.1617 0.1094\nuDmod1_AMORE::train_ADAPTgd_06 13:56:36.628 13:56:36.785 0.157 0.1736 0.1241\nuDmod1_AMORE::train_ADAPTgd_07 13:56:36.785 13:56:36.937 0.152 0.1962 0.1379\nuDmod1_AMORE::train_ADAPTgd_08 13:56:36.937 13:56:37.092 0.155 0.1366 0.1100\nuDmod1_AMORE::train_ADAPTgd_09 13:56:37.092 13:56:37.244 0.152 0.1247 0.1027\nuDmod1_AMORE::train_ADAPTgd_10 13:56:37.244 13:56:37.385 0.141 0.1717 0.1157\nuDmod1_AMORE::train_ADAPTgdwm_01 14:16:29.382 14:16:29.913 0.531 0.1034 0.0847\niter=10000 lr=0.009 momentum=0.8 sigmoid\nuDmod1_AMORE::train_ADAPTgdwm_02 14:16:29.913 14:16:30.444 0.531 0.1394 0.1006\nuDmod1_AMORE::train_ADAPTgdwm_03 14:16:30.444 14:16:30.975 0.531 0.0950 0.0811\nuDmod1_AMORE::train_ADAPTgdwm_04 14:16:30.975 14:16:31.507 0.532 0.0949 0.0798\nuDmod1_AMORE::train_ADAPTgdwm_05 14:16:31.507 14:16:32.038 0.531 0.0998 0.0838\nuDmod1_AMORE::train_ADAPTgdwm_06 14:16:32.038 14:16:32.569 0.531 0.1079 0.0870\nuDmod1_AMORE::train_ADAPTgdwm_07 14:16:32.569 14:16:33.131 0.562 0.1383 0.0981\nuDmod1_AMORE::train_ADAPTgdwm_08 14:16:33.147 14:16:33.662 0.515 0.1006 0.0829\nuDmod1_AMORE::train_ADAPTgdwm_09 14:16:33.662 14:16:34.193 0.531 0.1169 0.0954\nuDmod1_AMORE::train_ADAPTgdwm_10 14:16:34.193 14:16:34.709 0.516 0.1101 0.0921\nuDmod1_AMORE::train_BATCHgd_01 14:36:31.616 14:36:33.244 1.628 0.5136 0.4504 iter=7500 lr=0.05 sigmoid very bad\nuDmod1_AMORE::train_BATCHgd_02 14:36:33.244 14:36:34.835 1.591 0.4972 0.4339 iter=7500 lr=0.05 sigmoid very bad\nuDmod1_AMORE::train_BATCHgd_03 14:36:34.835 14:36:36.290 1.455 0.5132 0.4508 iter=7500 lr=0.05 sigmoid very bad\nuDmod1_AMORE::train_BATCHgd_04 14:36:36.290 14:36:37.542 1.252 0.5674 0.5073 iter=7500 lr=0.05 sigmoid very bad\nuDmod1_AMORE::train_BATCHgd_05 14:36:37.542 14:36:38.807 1.265 0.4722 0.4093 iter=7500 lr=0.05 sigmoid very bad\nuDmod1_AMORE::train_BATCHgdwm_01 15:03:29.098 15:03:32.286 3.188 0.5792 0.5167 iter=9000 lr=0.008 momentum=0.7 tansig Bad and no example\nuDmod1_AMORE::train_BATCHgdwm_02 15:03:32.286 15:03:35.281 2.995 0.5754 0.5144 iter=9000 lr=0.008 momentum=0.7 tansig Bad and no example\nuDmod1_AMORE::train_BATCHgdwm_03 15:03:35.281 15:03:38.228 2.947 0.5743 0.5138 iter=9000 lr=0.008 momentum=0.7 tansig Bad and no example\nuDmod1_AMORE::train_BATCHgdwm_04 15:03:38.228 15:03:41.316 3.088 0.5800 0.5168 iter=9000 lr=0.008 momentum=0.7 tansig Bad and no example\nuDmod1_AMORE::train_BATCHgdwm_05 15:03:41.316 15:03:44.318 3.002 0.5794 0.5169 iter=9000 lr=0.008 momentum=0.7 tansig Bad and no example\nuDmod1_ANN2::neuralnetwork_sgd_01 16:27:43.593 16:27:43.655 0.062 0.3381 0.2867\niter=4000 lr=0.01 Require fine tuning\nuDmod1_ANN2::neuralnetwork_sgd_02 16:27:43.655 16:27:43.718 0.063 0.3319 0.2894\nuDmod1_ANN2::neuralnetwork_sgd_03 16:27:43.718 16:27:43.780 0.062 0.3326 0.2900\nuDmod1_ANN2::neuralnetwork_sgd_04 16:27:43.796 16:27:43.858 0.062 0.2016 0.1621\nuDmod1_ANN2::neuralnetwork_sgd_05 16:27:43.858 16:27:43.921 0.063 0.2065 0.1621\nuDmod1_ANN2::neuralnetwork_sgd_06 16:27:43.937 16:27:43.999 0.062 0.2047 0.1608\nuDmod1_ANN2::neuralnetwork_sgd_07 16:27:44.015 16:27:44.077 0.062 0.2049 0.1600\nuDmod1_ANN2::neuralnetwork_sgd_08 16:27:44.077 16:27:44.140 0.063 0.3399 0.2990\nuDmod1_ANN2::neuralnetwork_sgd_09 16:27:44.140 16:27:44.218 0.078 0.3343 0.2928\nuDmod1_ANN2::neuralnetwork_sgd_10 16:27:44.218 16:27:44.280 0.062 0.2063 0.1634\nuDmod1_ANN2::neuralnetwork_adam_01 16:35:22.249 16:35:22.326 0.077 0.1481 0.1166\niter=4000 lr=0.02 Require fine tuning\nuDmod1_ANN2::neuralnetwork_adam_02 16:35:22.328 16:35:22.403 0.075 0.2052 0.1609\nuDmod1_ANN2::neuralnetwork_adam_03 16:35:22.405 16:35:22.482 0.077 0.2000 0.1599\nuDmod1_ANN2::neuralnetwork_adam_04 16:35:22.484 16:35:22.559 0.075 0.1813 0.1488\nuDmod1_ANN2::neuralnetwork_adam_05 16:35:22.561 16:35:22.638 0.077 0.3380 0.2870\nuDmod1_ANN2::neuralnetwork_adam_06 16:35:22.640 16:35:22.717 0.077 0.1670 0.1336\nuDmod1_ANN2::neuralnetwork_adam_07 16:35:22.719 16:35:22.794 0.075 0.1987 0.1559\nuDmod1_ANN2::neuralnetwork_adam_08 16:35:22.797 16:35:22.873 0.076 0.1846 0.1521\nuDmod1_ANN2::neuralnetwork_adam_09 16:35:22.875 16:35:22.951 0.076 0.1306 0.1072\nuDmod1_ANN2::neuralnetwork_adam_10 16:35:22.953 16:35:23.023 0.070 0.3344 0.2877\nuDmod1_ANN2::neuralnetwork_rmsprop_01 16:41:45.528 16:41:45.590 0.062 0.3447 0.2967 iter=4000 lr=0.03 Erratic\nuDmod1_ANN2::neuralnetwork_rmsprop_02 16:41:45.590 16:41:45.653 0.063 0.2279 0.1846 iter=4000 lr=0.03 Erratic\nuDmod1_ANN2::neuralnetwork_rmsprop_03 16:41:45.669 16:41:45.731 0.062 0.2011 0.1592 iter=4000 lr=0.03 Erratic\nuDmod1_ANN2::neuralnetwork_rmsprop_04 16:41:45.731 16:41:45.793 0.062 0.3362 0.2926 iter=4000 lr=0.03 Erratic\nuDmod1_ANN2::neuralnetwork_rmsprop_05 16:41:45.793 16:41:45.872 0.079 0.1988 0.1580 iter=4000 lr=0.03 Erratic\nuDmod1_ANN2::neuralnetwork_rmsprop_06 16:41:45.887 16:41:45.950 0.063 0.3377 0.2951 iter=4000 lr=0.03 Erratic\nuDmod1_ANN2::neuralnetwork_rmsprop_07 16:41:45.950 16:41:46.012 0.062 0.2090 0.1654 iter=4000 lr=0.03 Erratic\nuDmod1_ANN2::neuralnetwork_rmsprop_08 16:41:46.012 16:41:46.090 0.078 0.1834 0.1416 iter=4000 lr=0.03 Erratic\nuDmod1_ANN2::neuralnetwork_rmsprop_09 16:41:46.090 16:41:46.153 0.063 0.1703 0.1331 iter=4000 lr=0.03 Erratic\nuDmod1_ANN2::neuralnetwork_rmsprop_10 16:41:46.168 16:41:46.231 0.063 0.2080 0.1657 iter=4000 lr=0.03 Erratic\nuDmod1_deepnet::gradientdescent_01 17:29:50.544 17:29:50.669 0.125 0.2715 0.2264 numepochs=1000 Bad on univariate datasets\nuDmod1_deepnet::gradientdescent_02 17:29:50.669 17:29:50.794 0.125 0.3373 0.2886 numepochs=1000 Bad on univariate datasets\nuDmod1_deepnet::gradientdescent_03 17:29:50.794 17:29:50.919 0.125 0.2886 0.2362 numepochs=1000 Bad on univariate datasets\nuDmod1_deepnet::gradientdescent_04 17:29:50.919 17:29:51.044 0.125 0.3009 0.2505 numepochs=1000 Bad on univariate datasets\nuDmod1_deepnet::gradientdescent_05 17:29:51.044 17:29:51.169 0.125 0.2961 0.2528 numepochs=1000 Bad on univariate datasets\nuDmod1_deepnet::gradientdescent_06 17:29:51.169 17:29:51.294 0.125 0.2654 0.2177 numepochs=1000 Bad on univariate datasets\nuDmod1_deepnet::gradientdescent_07 17:29:51.310 17:29:51.435 0.125 0.2938 0.2382 numepochs=1000 Bad on univariate datasets\nuDmod1_deepnet::gradientdescent_08 17:29:51.435 17:29:51.560 0.125 0.2959 0.2524 numepochs=1000 Bad on univariate datasets\nuDmod1_deepnet::gradientdescent_09 17:29:51.560 17:29:51.685 0.125 0.2569 0.2049 numepochs=1000 Bad on univariate datasets\nuDmod1_deepnet::gradientdescent_10 17:29:51.685 17:29:51.810 0.125 0.3055 0.2500 numepochs=1000 Bad on univariate datasets\nuDmod1_MachineShop::fit.NNetModel_01 03:19:13.206 03:19:13.223 0.017 0.1133 0.0713 maxiter = 150 uses nnet\nuDmod1_MachineShop::fit.NNetModel_02 03:19:13.223 03:19:13.223 0.000 0.0627 0.0503 maxiter = 150 uses nnet\nuDmod1_MachineShop::fit.NNetModel_03 03:19:13.239 03:19:13.239 0.000 0.0449 0.0368 maxiter = 150 uses nnet\nuDmod1_MachineShop::fit.NNetModel_04 03:19:13.239 03:19:13.257 0.018 0.0928 0.0667 maxiter = 150 uses nnet\nuDmod1_MachineShop::fit.NNetModel_05 03:19:13.258 03:19:13.267 0.009 0.0645 0.0513 maxiter = 150 uses nnet\nuDmod1_MachineShop::fit.NNetModel_06 03:19:13.269 03:19:13.276 0.007 0.1114 0.0833 maxiter = 150 uses nnet\nuDmod1_MachineShop::fit.NNetModel_07 03:19:13.276 03:19:13.276 0.000 0.0963 0.0758 maxiter = 150 uses nnet\nuDmod1_MachineShop::fit.NNetModel_08 03:19:13.276 03:19:13.291 0.015 0.0443 0.0365 maxiter = 150 uses nnet\nuDmod1_MachineShop::fit.NNetModel_09 03:19:13.291 03:19:13.291 0.000 0.0660 0.0533 maxiter = 150 uses nnet\nuDmod1_MachineShop::fit.NNetModel_10 03:19:13.307 03:19:13.307 0.000 0.0447 0.0363 maxiter = 150 uses nnet\nuDmod1_momlp::monmlp.fit_BFGS_01 03:19:58.197 03:19:58.396 0.199 0.0557 0.0451 maxiter = 300 uses optimx\nuDmod1_momlp::monmlp.fit_BFGS_02 03:19:58.396 03:19:58.597 0.201 0.0520 0.0412 maxiter = 300 uses optimx\nuDmod1_momlp::monmlp.fit_BFGS_03 03:19:58.598 03:19:58.812 0.214 0.0503 0.0411 maxiter = 300 uses optimx\nuDmod1_momlp::monmlp.fit_BFGS_04 03:19:58.814 03:19:59.027 0.213 0.1029 0.0834 maxiter = 300 uses optimx\nuDmod1_momlp::monmlp.fit_BFGS_05 03:19:59.028 03:19:59.221 0.193 0.0905 0.0697 maxiter = 300 uses optimx\nuDmod1_momlp::monmlp.fit_BFGS_06 03:19:59.222 03:19:59.388 0.166 0.0877 0.0654 maxiter = 300 uses optimx\nuDmod1_momlp::monmlp.fit_BFGS_07 03:19:59.390 03:19:59.568 0.178 0.0502 0.0400 maxiter = 300 uses optimx\nuDmod1_momlp::monmlp.fit_BFGS_08 03:19:59.568 03:19:59.726 0.158 0.0636 0.0491 maxiter = 300 uses optimx\nuDmod1_momlp::monmlp.fit_BFGS_09 03:19:59.726 03:19:59.912 0.186 0.0659 0.0515 maxiter = 300 uses optimx\nuDmod1_momlp::monmlp.fit_BFGS_10 03:19:59.913 03:20:00.078 0.165 0.0504 0.0405 maxiter = 300 uses optimx\nuDmod1_momlp::monmlp.fit_Nelder-Mead_01 03:21:00.058 03:21:00.337 0.279 0.2013 0.1628 maxiter = 10000 uses optimx\nuDmod1_momlp::monmlp.fit_Nelder-Mead_02 03:21:00.337 03:21:00.679 0.342 0.1722 0.1318 maxiter = 10000 uses optimx\nuDmod1_momlp::monmlp.fit_Nelder-Mead_03 03:21:00.695 03:21:01.068 0.373 0.2230 0.1769 maxiter = 10000 uses optimx\nuDmod1_momlp::monmlp.fit_Nelder-Mead_04 03:21:01.069 03:21:01.387 0.318 0.2177 0.1694 maxiter = 10000 uses optimx\nuDmod1_momlp::monmlp.fit_Nelder-Mead_05 03:21:01.387 03:21:01.717 0.330 0.2824 0.2180 maxiter = 10000 uses optimx\nuDmod1_momlp::monmlp.fit_Nelder-Mead_06 03:21:01.717 03:21:02.075 0.358 0.1471 0.1172 maxiter = 10000 uses optimx\nuDmod1_momlp::monmlp.fit_Nelder-Mead_07 03:21:02.075 03:21:02.403 0.328 0.2066 0.1492 maxiter = 10000 uses optimx\nuDmod1_momlp::monmlp.fit_Nelder-Mead_08 03:21:02.403 03:21:02.755 0.352 0.2237 0.1817 maxiter = 10000 uses optimx\nuDmod1_momlp::monmlp.fit_Nelder-Mead_09 03:21:02.755 03:21:03.022 0.267 0.2876 0.2455 maxiter = 10000 uses optimx\nuDmod1_momlp::monmlp.fit_Nelder-Mead_10 03:21:03.022 03:21:03.368 0.346 0.1639 0.1310 maxiter = 10000 uses optimx\nuDmod1_neural::mlptrain_01 03:41:46.534 03:41:51.499 4.965 0.4647 0.3589 maxiter = 1000 classification\nuDmod1_neural::mlptrain_02 03:41:51.499 03:41:56.442 4.943 0.4647 0.3582 maxiter = 1000 classification\nuDmod1_neural::mlptrain_03 03:41:56.442 03:42:01.338 4.896 0.4647 0.3594 maxiter = 1000 classification\nuDmod1_neural::mlptrain_04 03:42:01.353 03:42:06.289 4.936 0.4647 0.3592 maxiter = 1000 classification\nuDmod1_neural::mlptrain_05 03:42:06.289 03:42:11.354 5.065 0.4647 0.3583 maxiter = 1000 classification\nuDmod1_neuralnet::neuralnet_backprop_01 03:52:09.894 03:52:14.210 4.316 0.5884 0.5069 Fails on difficult datasets\nuDmod1_neuralnet::neuralnet_backprop_02 03:52:14.211 03:52:18.614 4.403 0.5884 0.5069 Fails on difficult datasets\nuDmod1_neuralnet::neuralnet_backprop_03 03:52:18.615 03:52:22.875 4.260 0.5884 0.5069 Fails on difficult datasets\nuDmod1_neuralnet::neuralnet_backprop_04 03:52:22.891 03:52:27.134 4.243 0.5884 0.5069 Fails on difficult datasets\nuDmod1_neuralnet::neuralnet_backprop_05 03:52:27.150 03:52:31.408 4.258 0.5884 0.5069 Fails on difficult datasets\nuDmod1_neuralnet::neuralnet_rprop-_01 03:58:52.308 03:58:53.439 1.131 0.0614 0.0469 Erratic on multivariate datasets\nuDmod1_neuralnet::neuralnet_rprop-_02 03:58:53.439 03:58:55.384 1.945 0.0505 0.0422 Erratic on multivariate datasets\nuDmod1_neuralnet::neuralnet_rprop-_03 03:58:55.384 03:58:57.662 2.278 0.0511 0.0413 Erratic on multivariate datasets\nuDmod1_neuralnet::neuralnet_rprop-_04 03:58:57.663 03:58:59.247 1.584 0.0619 0.0496 Erratic on multivariate datasets\nuDmod1_neuralnet::neuralnet_rprop-_05 03:58:59.249 03:59:00.497 1.248 0.0468 0.0384 Erratic on multivariate datasets\nuDmod1_neuralnet::neuralnet_sag_01 04:11:20.663 04:11:26.040 5.377 0.5884 0.5069\nuDmod1_neuralnet::neuralnet_sag_02 04:11:26.040 04:11:31.529 5.489 0.5884 0.5069\nuDmod1_neuralnet::neuralnet_sag_03 04:11:31.529 04:11:37.130 5.601 0.5884 0.5069\nuDmod1_neuralnet::neuralnet_sag_04 04:11:37.130 04:11:42.512 5.382 0.5884 0.5069\nuDmod1_neuralnet::neuralnet_sag_05 04:11:42.512 04:11:47.945 5.433 0.5884 0.5069\nuDmod1_neuralnet::neuralnet_slr_01 04:19:14.053 04:19:14.378 0.325 0.0602 0.0456\nuDmod1_neuralnet::neuralnet_slr_02 04:19:14.378 04:19:14.966 0.588 0.0539 0.0431\nuDmod1_neuralnet::neuralnet_slr_03 04:19:14.966 04:19:15.615 0.649 0.0471 0.0382\nuDmod1_neuralnet::neuralnet_slr_04 04:19:15.615 04:19:19.680 4.065 0.0588 0.0457\nuDmod1_neuralnet::neuralnet_slr_05 04:19:19.680 04:19:20.949 1.269 0.0510 0.0414\nuDmod1_neuralnet::neuralnet_rprop+_01 04:24:31.266 04:24:32.040 0.774 0.0458 0.0360 Erratic on difficult datasets\nuDmod1_neuralnet::neuralnet_rprop+_02 04:24:32.042 04:24:33.620 1.578 0.0582 0.0439 Erratic on difficult datasets\nuDmod1_neuralnet::neuralnet_rprop+_03 04:24:33.620 04:24:37.195 3.575 0.1152 0.0863 Erratic on difficult datasets\nuDmod1_neuralnet::neuralnet_rprop+_04 04:24:37.195 04:24:42.531 5.336 0.5884 0.5069 Erratic on difficult datasets\nuDmod1_neuralnet::neuralnet_rprop+_05 04:24:42.531 04:24:43.894 1.363 0.0500 0.0394 Erratic on difficult datasets\nuDmod1_nnet::nnet_01 04:25:32.861 04:25:32.868 0.007 0.0847 0.0592 maxiter = 150 uses optim BFGS\nuDmod1_nnet::nnet_02 04:25:32.869 04:25:32.877 0.008 0.0451 0.0370 maxiter = 150 uses optim BFGS\nuDmod1_nnet::nnet_03 04:25:32.878 04:25:32.884 0.006 0.1103 0.0703 maxiter = 150 uses optim BFGS\nuDmod1_nnet::nnet_04 04:25:32.885 04:25:32.892 0.007 0.0458 0.0365 maxiter = 150 uses optim BFGS\nuDmod1_nnet::nnet_05 04:25:32.893 04:25:32.900 0.007 0.1667 0.1179 maxiter = 150 uses optim BFGS\nuDmod1_nnet::nnet_06 04:25:32.902 04:25:32.909 0.007 0.1131 0.0812 maxiter = 150 uses optim BFGS\nuDmod1_nnet::nnet_07 04:25:32.910 04:25:32.918 0.008 0.0480 0.0395 maxiter = 150 uses optim BFGS\nuDmod1_nnet::nnet_08 04:25:32.919 04:25:32.926 0.007 0.0442 0.0361 maxiter = 150 uses optim BFGS\nuDmod1_nnet::nnet_09 04:25:32.928 04:25:32.935 0.007 0.1014 0.0792 maxiter = 150 uses optim BFGS\nuDmod1_nnet::nnet_10 04:25:32.937 04:25:32.944 0.007 0.0827 0.0628 maxiter = 150 uses optim BFGS\nuDmod1_qrnn::qrnn.fit_01 04:25:55.062 04:25:55.253 0.191 0.0663 0.0448 maxiter = 700\nuDmod1_qrnn::qrnn.fit_02 04:25:55.253 04:25:55.636 0.383 0.1365 0.0715 maxiter = 700\nuDmod1_qrnn::qrnn.fit_03 04:25:55.636 04:25:55.835 0.199 0.0910 0.0634 maxiter = 700\nuDmod1_qrnn::qrnn.fit_04 04:25:55.835 04:25:56.026 0.191 0.0491 0.0326 maxiter = 700\nuDmod1_qrnn::qrnn.fit_05 04:25:56.042 04:25:56.241 0.199 0.1353 0.0711 maxiter = 700\nuDmod1_qrnn::qrnn.fit_06 04:25:56.241 04:25:56.474 0.233 0.1314 0.0675 maxiter = 700\nuDmod1_qrnn::qrnn.fit_07 04:25:56.474 04:25:56.846 0.372 0.0676 0.0434 maxiter = 700\nuDmod1_qrnn::qrnn.fit_08 04:25:56.846 04:25:57.162 0.316 0.1164 0.0601 maxiter = 700\nuDmod1_qrnn::qrnn.fit_09 04:25:57.162 04:25:57.550 0.388 0.0471 0.0327 maxiter = 700\nuDmod1_qrnn::qrnn.fit_10 04:25:57.550 04:25:57.810 0.260 0.0609 0.0382 maxiter = 700\nuDmod1_radiantmodel::radiantmodel_01 04:27:35.597 04:27:35.634 0.037 0.1029 0.0782 maxiter=10000 preset uses nnet\nuDmod1_radiantmodel::radiantmodel_02 04:27:35.636 04:27:35.672 0.036 0.0456 0.0363 maxiter=10000 preset uses nnet\nuDmod1_radiantmodel::radiantmodel_03 04:27:35.673 04:27:35.700 0.027 0.1168 0.0786 maxiter=10000 preset uses nnet\nuDmod1_radiantmodel::radiantmodel_04 04:27:35.701 04:27:35.733 0.032 0.1124 0.0705 maxiter=10000 preset uses nnet\nuDmod1_radiantmodel::radiantmodel_05 04:27:35.734 04:27:35.787 0.053 0.0433 0.0350 maxiter=10000 preset uses nnet\nuDmod1_radiantmodel::radiantmodel_06 04:27:35.788 04:27:35.824 0.036 0.0459 0.0363 maxiter=10000 preset uses nnet\nuDmod1_radiantmodel::radiantmodel_07 04:27:35.825 04:27:35.865 0.040 0.0403 0.0307 maxiter=10000 preset uses nnet\nuDmod1_radiantmodel::radiantmodel_08 04:27:35.866 04:27:35.909 0.043 0.0433 0.0349 maxiter=10000 preset uses nnet\nuDmod1_radiantmodel::radiantmodel_09 04:27:35.910 04:27:35.967 0.057 0.0433 0.0349 maxiter=10000 preset uses nnet\nuDmod1_radiantmodel::radiantmodel_10 04:27:35.967 04:27:35.999 0.032 0.0433 0.0350 maxiter=10000 preset uses nnet\nuDmod1_rcane::rlm_01 04:27:45.144 04:27:45.145 0.001 0.5885 0.5088 maxiter = 1000 linear only\nuDmod1_rcane::rlm_02 04:27:45.147 04:27:45.148 0.001 0.5885 0.5088 maxiter = 1000 linear only\nuDmod1_rcane::rlm_03 04:27:45.149 04:27:45.150 0.001 0.5885 0.5088 maxiter = 1000 linear only\nuDmod1_rcane::rlm_04 04:27:45.151 04:27:45.152 0.001 0.5885 0.5088 maxiter = 1000 linear only\nuDmod1_rcane::rlm_05 04:27:45.153 04:27:45.154 0.001 0.5885 0.5088 maxiter = 1000 linear only\nuDmod1_rcane::rlm_06 04:27:45.156 04:27:45.158 0.002 0.5885 0.5088 maxiter = 1000 linear only\nuDmod1_rcane::rlm_07 04:27:45.158 04:27:45.158 0.000 0.5885 0.5088 maxiter = 1000 linear only\nuDmod1_rcane::rlm_08 04:27:45.158 04:27:45.158 0.000 0.5885 0.5088 maxiter = 1000 linear only\nuDmod1_rcane::rlm_09 04:27:45.158 04:27:45.158 0.000 0.5885 0.5088 maxiter = 1000 linear only\nuDmod1_rcane::rlm_10 04:27:45.158 04:27:45.158 0.000 0.5885 0.5088 maxiter = 1000 linear only\nuDmod1_rminer::fit_01 04:27:57.919 04:27:57.936 0.017 0.0588 0.0434 maxiter = 150\nuDmod1_rminer::fit_02 04:27:57.937 04:27:57.955 0.018 0.0445 0.0362 maxiter = 150\nuDmod1_rminer::fit_03 04:27:57.956 04:27:57.974 0.018 0.0817 0.0695 maxiter = 150\nuDmod1_rminer::fit_04 04:27:57.975 04:27:57.991 0.016 0.0579 0.0475 maxiter = 150\nuDmod1_rminer::fit_05 04:27:57.993 04:27:58.010 0.017 0.0458 0.0364 maxiter = 150\nuDmod1_rminer::fit_06 04:27:58.012 04:27:58.030 0.018 0.0449 0.0366 maxiter = 150\nuDmod1_rminer::fit_07 04:27:58.031 04:27:58.050 0.019 0.0452 0.0358 maxiter = 150\nuDmod1_rminer::fit_08 04:27:58.052 04:27:58.068 0.016 0.0469 0.0375 maxiter = 150\nuDmod1_rminer::fit_09 04:27:58.070 04:27:58.089 0.019 0.0467 0.0378 maxiter = 150\nuDmod1_rminer::fit_10 04:27:58.090 04:27:58.108 0.018 0.0434 0.0341 maxiter = 150\nuDmod1_RSNNS::mlp_BackpropBatch_01 04:32:15.327 04:32:16.287 0.960 0.3805 0.3333 maxiter = 10000\nuDmod1_RSNNS::mlp_BackpropBatch_02 04:32:16.288 04:32:17.093 0.805 0.3194 0.2731 maxiter = 10000\nuDmod1_RSNNS::mlp_BackpropBatch_03 04:32:17.094 04:32:17.896 0.802 0.3076 0.2587 maxiter = 10000\nuDmod1_RSNNS::mlp_BackpropBatch_04 04:32:17.896 04:32:18.704 0.808 0.3368 0.2916 maxiter = 10000\nuDmod1_RSNNS::mlp_BackpropBatch_05 04:32:18.704 04:32:19.497 0.793 0.3328 0.2882 maxiter = 10000\nuDmod1_RSNNS::mlp_BackpropBatch_06 04:32:19.497 04:32:20.444 0.947 0.2564 0.1988 maxiter = 10000\nuDmod1_RSNNS::mlp_BackpropBatch_07 04:32:20.444 04:32:21.230 0.786 0.3781 0.3317 maxiter = 10000\nuDmod1_RSNNS::mlp_BackpropBatch_08 04:32:21.230 04:32:22.045 0.815 0.2913 0.2419 maxiter = 10000\nuDmod1_RSNNS::mlp_BackpropBatch_09 04:32:22.047 04:32:22.832 0.785 0.3322 0.2874 maxiter = 10000\nuDmod1_RSNNS::mlp_BackpropBatch_10 04:32:22.832 04:32:23.644 0.812 0.2746 0.2243 maxiter = 10000\nuDmod1_RSNNS::mlp_BackpropChunk_01 04:34:51.241 04:34:51.324 0.083 0.2654 0.2024 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropChunk_02 04:34:51.325 04:34:51.407 0.082 0.1944 0.1641 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropChunk_03 04:34:51.407 04:34:51.485 0.078 0.1600 0.1188 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropChunk_04 04:34:51.485 04:34:51.579 0.094 0.1599 0.1221 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropChunk_05 04:34:51.579 04:34:51.822 0.243 0.2258 0.1742 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropChunk_06 04:34:51.822 04:34:51.897 0.075 0.1368 0.0923 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropChunk_07 04:34:51.912 04:34:51.990 0.078 0.1924 0.1529 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropChunk_08 04:34:51.990 04:34:52.068 0.078 0.1693 0.1357 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropChunk_09 04:34:52.084 04:34:52.157 0.073 0.2190 0.1725 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropChunk_10 04:34:52.157 04:34:52.248 0.091 0.2115 0.1607 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropMomentum_01 04:35:28.577 04:35:28.659 0.082 0.2163 0.1720 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropMomentum_02 04:35:28.660 04:35:28.747 0.087 0.1320 0.0949 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropMomentum_03 04:35:28.748 04:35:28.830 0.082 0.1634 0.1272 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropMomentum_04 04:35:28.831 04:35:28.918 0.087 0.1454 0.1022 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropMomentum_05 04:35:28.920 04:35:29.007 0.087 0.1787 0.1505 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropMomentum_06 04:35:29.008 04:35:29.089 0.081 0.2018 0.1714 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropMomentum_07 04:35:29.091 04:35:29.178 0.087 0.1982 0.1536 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropMomentum_08 04:35:29.179 04:35:29.261 0.082 0.2053 0.1564 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropMomentum_09 04:35:29.263 04:35:29.342 0.079 0.1701 0.1284 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropMomentum_10 04:35:29.342 04:35:29.436 0.094 0.2109 0.1597 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropWeightDecay_01 04:36:06.630 04:36:06.712 0.082 0.1976 0.1563 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropWeightDecay_02 04:36:06.714 04:36:06.791 0.077 0.1769 0.1390 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropWeightDecay_03 04:36:06.791 04:36:06.877 0.086 0.1871 0.1533 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropWeightDecay_04 04:36:06.877 04:36:06.959 0.082 0.1423 0.1123 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropWeightDecay_05 04:36:06.959 04:36:07.053 0.094 0.1524 0.1154 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropWeightDecay_06 04:36:07.053 04:36:07.131 0.078 0.1507 0.1133 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropWeightDecay_07 04:36:07.131 04:36:07.222 0.091 0.1310 0.0920 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropWeightDecay_08 04:36:07.222 04:36:07.316 0.094 0.1489 0.1164 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropWeightDecay_09 04:36:07.316 04:36:07.393 0.077 0.1595 0.1153 maxiter = 1000\nuDmod1_RSNNS::mlp_BackpropWeightDecay_10 04:36:07.408 04:36:07.487 0.079 0.1557 0.1155 maxiter = 1000\nuDmod1_RSNNS::mlp_Quickprop_01 04:40:35.840 04:40:36.657 0.817 0.5884 0.5068 maxiter = 10000\nuDmod1_RSNNS::mlp_Quickprop_02 04:40:36.658 04:40:37.596 0.938 0.5884 0.5069 maxiter = 10000\nuDmod1_RSNNS::mlp_Quickprop_03 04:40:37.596 04:40:38.393 0.797 0.5884 0.5070 maxiter = 10000\nuDmod1_RSNNS::mlp_Quickprop_04 04:40:38.393 04:40:39.209 0.816 0.5884 0.5071 maxiter = 10000\nuDmod1_RSNNS::mlp_Quickprop_05 04:40:39.209 04:40:39.998 0.789 0.5884 0.5069 maxiter = 10000\nuDmod1_RSNNS::mlp_Quickprop_06 04:40:39.999 04:40:40.957 0.958 0.5885 0.5075 maxiter = 10000\nuDmod1_RSNNS::mlp_Quickprop_07 04:40:40.958 04:40:41.827 0.869 0.5884 0.5068 maxiter = 10000\nuDmod1_RSNNS::mlp_Quickprop_08 04:40:41.828 04:40:42.606 0.778 0.5884 0.5069 maxiter = 10000\nuDmod1_RSNNS::mlp_Quickprop_09 04:40:42.607 04:40:43.415 0.808 0.5885 0.5075 maxiter = 10000\nuDmod1_RSNNS::mlp_Quickprop_10 04:40:43.415 04:40:44.215 0.800 0.5884 0.5070 maxiter = 10000\nuDmod1_RSNNS::mlp_Rprop_01 04:43:20.964 04:43:21.042 0.078 0.0667 0.0515 maxiter = 1000\nuDmod1_RSNNS::mlp_Rprop_02 04:43:21.042 04:43:21.133 0.091 0.1403 0.1012 maxiter = 1000\nuDmod1_RSNNS::mlp_Rprop_03 04:43:21.133 04:43:21.211 0.078 0.1204 0.0819 maxiter = 1000\nuDmod1_RSNNS::mlp_Rprop_04 04:43:21.211 04:43:21.302 0.091 0.0620 0.0486 maxiter = 1000\nuDmod1_RSNNS::mlp_Rprop_05 04:43:21.302 04:43:21.380 0.078 0.1315 0.0833 maxiter = 1000\nuDmod1_RSNNS::mlp_Rprop_06 04:43:21.380 04:43:21.473 0.093 0.1240 0.0828 maxiter = 1000\nuDmod1_RSNNS::mlp_Rprop_07 04:43:21.473 04:43:21.552 0.079 0.4062 0.2843 maxiter = 1000\nuDmod1_RSNNS::mlp_Rprop_08 04:43:21.552 04:43:21.642 0.090 0.1330 0.0861 maxiter = 1000\nuDmod1_RSNNS::mlp_Rprop_09 04:43:21.642 04:43:21.726 0.084 0.0505 0.0400 maxiter = 1000\nuDmod1_RSNNS::mlp_Rprop_10 04:43:21.726 04:43:21.804 0.078 0.3232 0.2421 maxiter = 1000\nuDmod1_RSNNS::mlp_SCG_01 04:44:13.711 04:44:13.840 0.129 0.1720 0.1301 maxiter = 1000\nuDmod1_RSNNS::mlp_SCG_02 04:44:13.840 04:44:13.981 0.141 0.0846 0.0659 maxiter = 1000\nuDmod1_RSNNS::mlp_SCG_03 04:44:13.981 04:44:14.121 0.140 0.1293 0.0883 maxiter = 1000\nuDmod1_RSNNS::mlp_SCG_04 04:44:14.121 04:44:14.262 0.141 0.1396 0.1038 maxiter = 1000\nuDmod1_RSNNS::mlp_SCG_05 04:44:14.262 04:44:14.400 0.138 0.1414 0.1023 maxiter = 1000\nuDmod1_RSNNS::mlp_SCG_06 04:44:14.400 04:44:14.540 0.140 0.0569 0.0466 maxiter = 1000\nuDmod1_RSNNS::mlp_SCG_07 04:44:14.540 04:44:14.634 0.094 0.4021 0.3555 maxiter = 1000\nuDmod1_RSNNS::mlp_SCG_08 04:44:14.634 04:44:14.753 0.119 0.1967 0.1424 maxiter = 1000\nuDmod1_RSNNS::mlp_SCG_09 04:44:14.753 04:44:14.862 0.109 0.1458 0.1040 maxiter = 1000\nuDmod1_RSNNS::mlp_SCG_10 04:44:14.862 04:44:15.003 0.141 0.1122 0.0825 maxiter = 1000\nuDmod1_RSNNS::mlp_Std_Backpropagation_01 04:44:58.260 04:44:58.343 0.083 0.1763 0.1290 maxiter = 1000\nuDmod1_RSNNS::mlp_Std_Backpropagation_02 04:44:58.343 04:44:58.429 0.086 0.1936 0.1454 maxiter = 1000\nuDmod1_RSNNS::mlp_Std_Backpropagation_03 04:44:58.430 04:44:58.515 0.085 0.1844 0.1375 maxiter = 1000\nuDmod1_RSNNS::mlp_Std_Backpropagation_04 04:44:58.515 04:44:58.599 0.084 0.2419 0.1921 maxiter = 1000\nuDmod1_RSNNS::mlp_Std_Backpropagation_05 04:44:58.601 04:44:58.688 0.087 0.1657 0.1228 maxiter = 1000\nuDmod1_RSNNS::mlp_Std_Backpropagation_06 04:44:58.689 04:44:58.770 0.081 0.1436 0.1074 maxiter = 1000\nuDmod1_RSNNS::mlp_Std_Backpropagation_07 04:44:58.772 04:44:58.858 0.086 0.1442 0.1115 maxiter = 1000\nuDmod1_RSNNS::mlp_Std_Backpropagation_08 04:44:58.859 04:44:58.945 0.086 0.2082 0.1756 maxiter = 1000\nuDmod1_RSNNS::mlp_Std_Backpropagation_09 04:44:58.947 04:44:59.029 0.082 0.2326 0.2057 maxiter = 1000\nuDmod1_RSNNS::mlp_Std_Backpropagation_10 04:44:59.030 04:44:59.116 0.086 0.2260 0.1941 maxiter = 1000\nuDmod1_simpleNeural::sN.MLPtrain_01 04:49:29.834 04:49:30.018 0.184 0.5846 0.5052 maxiter = 1000 for bi- or multiclass classification\nuDmod1_simpleNeural::sN.MLPtrain_02 04:49:30.020 04:49:30.208 0.188 0.5846 0.5052 maxiter = 1000 for bi- or multiclass classification\nuDmod1_simpleNeural::sN.MLPtrain_03 04:49:30.209 04:49:30.386 0.177 0.5846 0.5052 maxiter = 1000 for bi- or multiclass classification\nuDmod1_simpleNeural::sN.MLPtrain_04 04:49:30.386 04:49:30.574 0.188 0.5846 0.5052 maxiter = 1000 for bi- or multiclass classification\nuDmod1_simpleNeural::sN.MLPtrain_05 04:49:30.574 04:49:30.758 0.184 0.5846 0.5052 maxiter = 1000 for bi- or multiclass classification\nuDmod1_simpleNeural::sN.MLPtrain_06 04:49:30.758 04:49:30.957 0.199 0.5846 0.5052 maxiter = 1000 for bi- or multiclass classification\nuDmod1_simpleNeural::sN.MLPtrain_07 04:49:30.961 04:49:31.150 0.189 0.5846 0.5052 maxiter = 1000 for bi- or multiclass classification\nuDmod1_simpleNeural::sN.MLPtrain_08 04:49:31.150 04:49:31.343 0.193 0.5846 0.5052 maxiter = 1000 for bi- or multiclass classification\nuDmod1_simpleNeural::sN.MLPtrain_09 04:49:31.345 04:49:31.521 0.176 0.5846 0.5052 maxiter = 1000 for bi- or multiclass classification\nuDmod1_simpleNeural::sN.MLPtrain_10 04:49:31.521 04:49:31.714 0.193 0.5846 0.5052 maxiter = 1000 for bi- or multiclass classification\nuDmod1_snnR::snnR_01 04:49:59.086 04:49:59.111 0.025 0.3595 0.3116 maxiter = 200 better on multi\nuDmod1_snnR::snnR_02 04:49:59.127 04:49:59.143 0.016 0.2927 0.2512 maxiter = 200 better on multi\nuDmod1_snnR::snnR_03 04:49:59.143 04:49:59.172 0.029 0.2927 0.2512 maxiter = 200 better on multi\nuDmod1_snnR::snnR_04 04:49:59.174 04:49:59.194 0.020 0.2927 0.2512 maxiter = 200 better on multi\nuDmod1_snnR::snnR_05 04:49:59.194 04:49:59.258 0.064 0.3595 0.3116 maxiter = 200 better on multi\nuDmod1_snnR::snnR_06 04:49:59.259 04:49:59.271 0.012 0.2927 0.2512 maxiter = 200 better on multi\nuDmod1_snnR::snnR_07 04:49:59.287 04:49:59.323 0.036 0.3595 0.3116 maxiter = 200 better on multi\nuDmod1_snnR::snnR_08 04:49:59.325 04:49:59.344 0.019 0.2927 0.2512 maxiter = 200 better on multi\nuDmod1_snnR::snnR_09 04:49:59.345 04:49:59.364 0.019 0.2927 0.2512 maxiter = 200 better on multi\nuDmod1_snnR::snnR_10 04:49:59.364 04:49:59.407 0.043 0.3595 0.3116 maxiter = 200 better on multi\nuDmod1_softmaxreg::softmaxreg_01 04:50:01.720 04:50:01.720 0.000 0.5884 0.5069 maxiter = 1000\nuDmod1_softmaxreg::softmaxreg_02 04:50:01.720 04:50:01.720 0.000 0.5884 0.5069 maxiter = 1000\nuDmod1_softmaxreg::softmaxreg_03 04:50:01.736 04:50:01.736 0.000 0.5884 0.5069 maxiter = 1000\nuDmod1_softmaxreg::softmaxreg_04 04:50:01.736 04:50:01.736 0.000 0.5884 0.5069 maxiter = 1000\nuDmod1_softmaxreg::softmaxreg_05 04:50:01.736 04:50:01.736 0.000 0.5884 0.5069 maxiter = 1000\nuDmod1_softmaxreg::softmaxreg_06 04:50:01.736 04:50:01.736 0.000 0.5884 0.5069 maxiter = 1000\nuDmod1_softmaxreg::softmaxreg_07 04:50:01.736 04:50:01.736 0.000 0.5884 0.5069 maxiter = 1000\nuDmod1_softmaxreg::softmaxreg_08 04:50:01.736 04:50:01.736 0.000 0.5884 0.5069 maxiter = 1000\nuDmod1_softmaxreg::softmaxreg_09 04:50:01.736 04:50:01.751 0.015 0.5884 0.5069 maxiter = 1000\nuDmod1_softmaxreg::softmaxreg_10 04:50:01.751 04:50:01.751 0.000 0.5884 0.5069 maxiter = 1000\nuDmod1_tsensembler::tsensembler_01 04:50:59.194 04:50:59.286 0.092 0.1147 0.0890 maxiter = 150 uses nnet\nuDmod1_tsensembler::tsensembler_02 04:50:59.287 04:50:59.405 0.118 0.1147 0.0890 maxiter = 150 uses nnet\nuDmod1_tsensembler::tsensembler_03 04:50:59.405 04:50:59.507 0.102 0.1147 0.0890 maxiter = 150 uses nnet\nuDmod1_tsensembler::tsensembler_04 04:50:59.508 04:50:59.587 0.079 0.1149 0.0891 maxiter = 150 uses nnet\nuDmod1_tsensembler::tsensembler_05 04:50:59.589 04:50:59.659 0.070 0.1147 0.0891 maxiter = 150 uses nnet\nuDmod1_tsensembler::tsensembler_06 04:50:59.661 04:50:59.728 0.067 0.1148 0.0893 maxiter = 150 uses nnet\nuDmod1_tsensembler::tsensembler_07 04:50:59.729 04:50:59.794 0.065 0.1146 0.0890 maxiter = 150 uses nnet\nuDmod1_tsensembler::tsensembler_08 04:50:59.794 04:50:59.844 0.050 0.2116 0.1633 maxiter = 150 uses nnet\nuDmod1_tsensembler::tsensembler_09 04:50:59.844 04:50:59.951 0.107 0.1147 0.0890 maxiter = 150 uses nnet\nuDmod1_tsensembler::tsensembler_10 04:50:59.953 04:51:00.022 0.069 0.1147 0.0890 maxiter = 150 uses nnet\nuDmod1_validann::ann_BFGS_01 04:53:24.869 04:53:25.578 0.709 0.0532 0.0444 maxiter = 200 optim\nuDmod1_validann::ann_BFGS_02 04:53:25.579 04:53:26.286 0.707 0.0460 0.0372 maxiter = 200 optim\nuDmod1_validann::ann_BFGS_03 04:53:26.291 04:53:26.995 0.704 0.1131 0.0715 maxiter = 200 optim\nuDmod1_validann::ann_BFGS_04 04:53:26.997 04:53:27.700 0.703 0.0569 0.0447 maxiter = 200 optim\nuDmod1_validann::ann_BFGS_05 04:53:27.701 04:53:28.409 0.708 0.1144 0.0785 maxiter = 200 optim\nuDmod1_validann::ann_BFGS_06 04:53:28.409 04:53:29.113 0.704 0.1156 0.0782 maxiter = 200 optim\nuDmod1_validann::ann_BFGS_07 04:53:29.114 04:53:29.826 0.712 0.1174 0.0776 maxiter = 200 optim\nuDmod1_validann::ann_BFGS_08 04:53:29.828 04:53:30.523 0.695 0.0522 0.0368 maxiter = 200 optim\nuDmod1_validann::ann_BFGS_09 04:53:30.523 04:53:31.224 0.701 0.0885 0.0689 maxiter = 200 optim\nuDmod1_validann::ann_BFGS_10 04:53:31.224 04:53:31.930 0.706 0.0420 0.0326 maxiter = 200 optim\nuDmod1_validann::ann_CG_01 05:03:15.638 05:03:20.274 4.636 0.0580 0.0478 maxiter = 1000 optim\nuDmod1_validann::ann_CG_02 05:03:20.274 05:03:24.900 4.626 0.0600 0.0495 maxiter = 1000 optim\nuDmod1_validann::ann_CG_03 05:03:24.901 05:03:29.529 4.628 0.0732 0.0612 maxiter = 1000 optim\nuDmod1_validann::ann_CG_04 05:03:29.529 05:03:34.225 4.696 0.0506 0.0406 maxiter = 1000 optim\nuDmod1_validann::ann_CG_05 05:03:34.225 05:03:38.826 4.601 0.0649 0.0521 maxiter = 1000 optim\nuDmod1_validann::ann_CG_06 05:03:38.828 05:03:43.452 4.624 0.0792 0.0632 maxiter = 1000 optim\nuDmod1_validann::ann_CG_07 05:03:43.452 05:03:48.102 4.650 0.1111 0.0687 maxiter = 1000 optim\nuDmod1_validann::ann_CG_08 05:03:48.105 05:03:52.687 4.582 0.0522 0.0431 maxiter = 1000 optim\nuDmod1_validann::ann_CG_09 05:03:52.687 05:03:57.295 4.608 0.0817 0.0668 maxiter = 1000 optim\nuDmod1_validann::ann_CG_10 05:03:57.295 05:04:02.003 4.708 0.0496 0.0398 maxiter = 1000 optim\nuDmod1_validann::ann_L-BFGS-B_01 05:09:37.070 05:09:37.820 0.750 0.1422 0.1054 maxiter = 200 optim\nuDmod1_validann::ann_L-BFGS-B_02 05:09:37.835 05:09:38.623 0.788 0.0668 0.0554 maxiter = 200 optim\nuDmod1_validann::ann_L-BFGS-B_03 05:09:38.624 05:09:39.462 0.838 0.0726 0.0614 maxiter = 200 optim\nuDmod1_validann::ann_L-BFGS-B_04 05:09:39.478 05:09:40.255 0.777 0.0665 0.0493 maxiter = 200 optim\nuDmod1_validann::ann_L-BFGS-B_05 05:09:40.256 05:09:41.039 0.783 0.0520 0.0427 maxiter = 200 optim\nuDmod1_validann::ann_L-BFGS-B_06 05:09:41.040 05:09:41.833 0.793 0.1158 0.0756 maxiter = 200 optim\nuDmod1_validann::ann_L-BFGS-B_07 05:09:41.834 05:09:42.706 0.872 0.1173 0.0777 maxiter = 200 optim\nuDmod1_validann::ann_L-BFGS-B_08 05:09:42.706 05:09:43.531 0.825 0.1233 0.0831 maxiter = 200 optim\nuDmod1_validann::ann_L-BFGS-B_09 05:09:43.531 05:09:44.328 0.797 0.1064 0.0817 maxiter = 200 optim\nuDmod1_validann::ann_L-BFGS-B_10 05:09:44.328 05:09:45.123 0.795 0.0807 0.0632 maxiter = 200 optim\nuDmod1_validann::ann_Nelder-Mead_01 05:11:53.062 05:11:53.999 0.937 0.1921 0.1499 maxiter = 10000 optim\nuDmod1_validann::ann_Nelder-Mead_02 05:11:53.999 05:11:55.164 1.165 0.1857 0.1512 maxiter = 10000 optim\nuDmod1_validann::ann_Nelder-Mead_03 05:11:55.166 05:11:56.346 1.180 0.2251 0.1750 maxiter = 10000 optim\nuDmod1_validann::ann_Nelder-Mead_04 05:11:56.346 05:11:57.504 1.158 0.1858 0.1527 maxiter = 10000 optim\nuDmod1_validann::ann_Nelder-Mead_05 05:11:57.504 05:11:58.795 1.291 0.1889 0.1437 maxiter = 10000 optim\nuDmod1_validann::ann_Nelder-Mead_06 05:11:58.796 05:11:59.937 1.141 0.1497 0.1230 maxiter = 10000 optim\nuDmod1_validann::ann_Nelder-Mead_07 05:11:59.939 05:12:01.092 1.153 0.3195 0.2348 maxiter = 10000 optim\nuDmod1_validann::ann_Nelder-Mead_08 05:12:01.092 05:12:02.170 1.078 0.2090 0.1549 maxiter = 10000 optim\nuDmod1_validann::ann_Nelder-Mead_09 05:12:02.170 05:12:03.070 0.900 0.1782 0.1417 maxiter = 10000 optim\nuDmod1_validann::ann_Nelder-Mead_10 05:12:03.071 05:12:04.253 1.182 0.2264 0.1624 maxiter = 10000 optim\nuDmod1_validann::ann_SANN_01 05:14:06.814 05:14:07.894 1.080 0.3134 0.2526 maxiter = 10000 optim\nuDmod1_validann::ann_SANN_02 05:14:07.895 05:14:08.846 0.951 0.2621 0.2201 maxiter = 10000 optim\nuDmod1_validann::ann_SANN_03 05:14:08.847 05:14:09.793 0.946 0.2560 0.2046 maxiter = 10000 optim\nuDmod1_validann::ann_SANN_04 05:14:09.795 05:14:10.733 0.938 0.3248 0.2664 maxiter = 10000 optim\nuDmod1_validann::ann_SANN_05 05:14:10.733 05:14:11.688 0.955 0.2888 0.2387 maxiter = 10000 optim\nuDmod1_validann::ann_SANN_06 05:14:11.688 05:14:12.660 0.972 0.2870 0.2420 maxiter = 10000 optim\nuDmod1_validann::ann_SANN_07 05:14:12.661 05:14:13.621 0.960 0.3616 0.2892 maxiter = 10000 optim\nuDmod1_validann::ann_SANN_08 05:14:13.622 05:14:14.562 0.940 0.2756 0.2334 maxiter = 10000 optim\nuDmod1_validann::ann_SANN_09 05:14:14.577 05:14:15.531 0.954 0.3037 0.2475 maxiter = 10000 optim\nuDmod1_validann::ann_SANN_10 05:14:15.531 05:14:16.483 0.952 0.3061 0.2618 maxiter = 10000 optim\nuDmod2_AMORE::train_ADAPTgdwm_09 0.478 0.0499 0.0411\nuDmod2_ANN2::neuralnetwork_adam_10 0.070 0.0720 0.0576\niter=4000 lr=0.02 Require fine tuning ANN2\nuDmod2_brnn::brnn_gaussNewton_03 0.031 0.0438 0.0351 *** Different NN brnn\nuDmod2_deepnet::gradientdescent_03 0.109 0.1427 0.1140 numepochs=1000 Bad on univariate datasets deepnet\nuDmod2_h2o::h2o.deeplearning_05 0.469 0.1970 0.1619 maxiter=200 Bad on univariate datasets h2o\nuDmod2_MachineShop::fit.NNetModel_07 0.000 0.0406 0.0331 maxiter = 150 uses nnet MachineShop\nuDmod2_minpacklm::nlsLM_03 0.015 0.0427 0.0333 ** maxiter = 150 Require hand-made formulas and scaling minpacklm\nuDmod2_momlp::monmlp.fit_BFGS_09 0.164 0.0473 0.0378 maxiter = 300 uses optimx momlp\nuDmod2_neural::mlptrain_01 4.489 0.3865 0.2679 maxiter = 1000 classification neural\nuDmod2_neuralnet::neuralnet_rprop+_05 2.296 0.0417 0.0334 Erratic on difficult datasets neuralnet\nuDmod2_nlsr::nlxb_10 0.078 0.0405 0.0327 ** maxiter = 150 Require hand-made formulas and scaling nlsr\nuDmod2_nnet::nnet_07 0.008 0.0406 0.0324 maxiter = 150 uses optim BFGS nnet\nuDmod2_qrnn::qrnn.fit_06 0.556 0.0449 0.0305 maxiter = 700 qrnn\nuDmod2_radiantmodel::radiantmodel_01 0.028 0.0405 0.0327 maxiter=10000 preset uses nnet radiantmodel\nuDmod2_rcane::rlm_01 0.015 0.5179 0.4264 maxiter = 1000 linear only rcane\nuDmod2_rminer::fit_02 0.016 0.0405 0.0327 maxiter = 150 rminer\nuDmod2_RSNNS::mlp_SCG_10 0.133 0.0562 0.0461 maxiter = 1000 RSNNS\nuDmod2_simpleNeural::sN.MLPtrain_01 0.169 0.5547 0.4906 maxiter = 1000 for bi- or multiclass classification simpleNeural\nuDmod2_snnR::snnR_01 0.023 0.2585 0.2264 maxiter = 200 better on multi snnR\nuDmod2_softmaxreg::softmaxreg_01 0.000 0.5176 0.4253 maxiter = 1000 softmaxreg\nuDmod2_tsensembler::tsensembler_01 0.078 0.0734 0.0557 maxiter = 150 uses nnet tsensembler\nuDmod2_validann::ann_BFGS_08 0.588 0.0405 0.0327 maxiter = 200 optim validann\nuDreyfus1_AMORE::train_ADAPTgdwm_01 0.385 0.0435 0.0347\nuDreyfus1_ANN2::neuralnetwork_adam_05 0.056 0.0172 0.0135\nuDreyfus1_brnn::brnn_gaussNewton_01 0.000 0.0301 0.0235 *** Different NN brnn\nuDreyfus1_deepnet::gradientdescent_02 0.110 0.2631 0.2096 numepochs=1000 Bad on univariate datasets deepnet\nuDreyfus1_h2o::h2o.deeplearning_05 0.578 0.2813 0.2409 maxiter=200 Bad on univariate datasets h2o\nuDreyfus1_MachineShop::fit.NNetModel_03 0.000 0.0021 0.0017 maxiter = 150 uses nnet MachineShop\nuDreyfus1_minpacklm::nlsLM_01 0.000 0.0000 0.0000 ** maxiter = 150 Require hand-made formulas and scaling minpacklm\nuDreyfus1_momlp::monmlp.fit_BFGS_07 0.151 0.0019 0.0013 maxiter = 300 uses optimx momlp\nuDreyfus1_neural::mlptrain_01 2.901 1.0568 0.9326 maxiter = 1000 classification neural\nuDreyfus1_neuralnet::neuralnet_slr_04 0.094 0.0093 0.0081 neuralnet\nuDreyfus1_nlsr::nlxb_01 0.032 0.0000 0.0000 ** maxiter = 150 Require hand-made formulas and scaling nlsr\nuDreyfus1_nnet::nnet_04 0.005 0.0020 0.0016 maxiter = 150 uses optim BFGS nnet\nuDreyfus1_qrnn::qrnn.fit_03 0.144 0.0000 0.0000 maxiter = 700 qrnn\nuDreyfus1_radiantmodel::radiantmodel_09 0.022 0.0044 0.0030 maxiter=10000 preset uses nnet radiantmodel\nuDreyfus1_rcane::rlm_01 0.001 1.6305 1.2963 maxiter = 1000 linear only rcane\nuDreyfus1_rminer::fit_01 0.009 0.0018 0.0015 maxiter = 150 rminer\nuDreyfus1_RSNNS::mlp_Rprop_06 0.078 0.0619 0.0456 maxiter = 1000 RSNNS\nuDreyfus1_simpleNeural::sN.MLPtrain_01 0.153 0.5027 0.3467 maxiter = 1000 for bi- or multiclass classification simpleNeural\nuDreyfus1_snnR::snnR_01 0.010 0.3691 0.2756 maxiter = 200 better on multi snnR\nuDreyfus1_softmaxreg::softmaxreg_01 0.000 1.6122 1.3725 maxiter = 1000 softmaxreg\nuDreyfus1_tsensembler::tsensembler_04 0.058 0.1266 0.0850 maxiter = 150 uses nnet tsensembler\nuDreyfus1_validann::ann_BFGS_09 0.149 0.0020 0.0016 maxiter = 200 optim validann\nuDreyfus2_AMORE::train_ADAPTgdwm_08 0.384 0.1002 0.0792\nuDreyfus2_ANN2::neuralnetwork_adam_07 0.055 0.0981 0.0767\nuDreyfus2_brnn::brnn_gaussNewton_01 0.000 0.0971 0.0776 *** Different NN brnn\nuDreyfus2_deepnet::gradientdescent_09 0.109 0.2790 0.2144 numepochs=1000 Bad on univariate datasets deepnet\nuDreyfus2_h2o::h2o.deeplearning_10 0.501 0.2717 0.2102 maxiter=200 Bad on univariate datasets h2o\nuDreyfus2_MachineShop::fit.NNetModel_03 0.006 0.0906 0.0725 maxiter = 150 uses nnet MachineShop\nuDreyfus2_minpacklm::nlsLM_01 0.016 0.0906 0.0723 ** maxiter = 150 Require hand-made formulas and scaling minpacklm\nuDreyfus2_momlp::monmlp.fit_BFGS_01 0.156 0.0910 0.0729 maxiter = 300 uses optimx momlp\nuDreyfus2_neural::mlptrain_01 2.921 1.0574 0.9265 maxiter = 1000 classification neural\nuDreyfus2_neuralnet::neuralnet_rprop-_03 0.093 0.0913 0.0720 Erratic on multivariate datasets neuralnet\nuDreyfus2_nlsr::nlxb_01 0.062 0.0906 0.0723 ** maxiter = 150 Require hand-made formulas and scaling nlsr\nuDreyfus2_nnet::nnet_06 0.005 0.0906 0.0724 maxiter = 150 uses optim BFGS nnet\nuDreyfus2_qrnn::qrnn.fit_03 0.191 0.0956 0.0684 maxiter = 700 qrnn\nuDreyfus2_radiantmodel::radiantmodel_02 0.023 0.0906 0.0723 maxiter=10000 preset uses nnet radiantmodel\nuDreyfus2_rcane::rlm_01 0.000 1.6298 1.2982 maxiter = 1000 linear only rcane\nuDreyfus2_rminer::fit_02 0.015 0.0906 0.0724 maxiter = 150 rminer\nuDreyfus2_RSNNS::mlp_Rprop_04 0.078 0.1122 0.0886 maxiter = 1000 RSNNS\nuDreyfus2_simpleNeural::sN.MLPtrain_01 0.154 0.4931 0.3545 maxiter = 1000 for bi- or multiclass classification simpleNeural\nuDreyfus2_snnR::snnR_01 0.010 0.3837 0.2773 maxiter = 200 better on multi snnR\nuDreyfus2_softmaxreg::softmaxreg_01 0.000 1.6087 1.3690 maxiter = 1000 softmaxreg\nuDreyfus2_tsensembler::tsensembler_09 0.072 0.1530 0.1109 maxiter = 150 uses nnet tsensembler\nuDreyfus2_validann::ann_BFGS_01 0.232 0.0906 0.0724 maxiter = 200 optim validann\nuGauss1_AMORE::train_ADAPTgdwm_08 1.156 2.3558 1.8756\nuGauss1_ANN2::neuralnetwork_adam_03 0.467 2.3603 1.8819\nuGauss1_brnn::brnn_gaussNewton_03 0.047 2.2447 1.7473 *** Different NN brnn\nuGauss1_deepnet::gradientdescent_09 0.391 6.0340 4.8565 numepochs=1000 Bad on univariate datasets deepnet\nuGauss1_h2o::h2o.deeplearning_07 0.516 9.5879 7.8046 maxiter=200 Bad on univariate datasets h2o\nuGauss1_MachineShop::fit.NNetModel_07 0.016 2.2456 1.7438 maxiter = 150 uses nnet MachineShop\nuGauss1_minpacklm::nlsLM_07 0.078 2.2282 1.7250 ** maxiter = 150 Require hand-made formulas and scaling minpacklm\nuGauss1_momlp::monmlp.fit_BFGS_09 0.185 2.3281 1.8434 maxiter = 300 uses optimx momlp\nuGauss1_neural::mlptrain_01 21.022 31.5754 27.2849 maxiter = 1000 classification neural\nuGauss1_neuralnet::neuralnet_rprop+_01 2.129 2.2509 1.7664 Erratic on difficult datasets neuralnet\nuGauss1_nlsr::nlxb_02 0.172 2.2333 1.7391 ** maxiter = 150 Require hand-made formulas and scaling nlsr\nuGauss1_nnet::nnet_09 0.060 2.2384 1.7372 maxiter = 150 uses optim BFGS nnet\nuGauss1_qrnn::qrnn.fit_08 0.287 2.2468 1.7022 maxiter = 700 qrnn\nuGauss1_radiantmodel::radiantmodel_05 0.093 2.2289 1.7264 maxiter=10000 preset uses nnet radiantmodel\nuGauss1_rcane::rlm_01 0.000 73.4624 60.5314 maxiter = 1000 linear only rcane\nuGauss1_rminer::fit_07 0.068 2.2347 1.7381 maxiter = 150 rminer\nuGauss1_RSNNS::mlp_BackpropChunk_02 0.310 2.7267 2.1229 maxiter = 1000 RSNNS\nuGauss1_simpleNeural::sN.MLPtrain_01 0.492 0.4805 0.3934 maxiter = 1000 for bi- or multiclass classification simpleNeural\nuGauss1_snnR::snnR_05 0.057 6.1846 5.1406 maxiter = 200 better on multi snnR\nuGauss1_softmaxreg::softmaxreg_01 0.000 41.6253 36.1679 maxiter = 1000 softmaxreg\nuGauss1_tsensembler::tsensembler_09 0.479 2.7080 2.1559 maxiter = 150 uses nnet tsensembler\nuGauss1_validann::ann_BFGS_01 0.781 2.2356 1.7373 maxiter = 200 optim validann\nuGauss2_AMORE::train_ADAPTgdwm_08 0.969 2.9516 2.3865\nuGauss2_ANN2::neuralnetwork_adam_01 0.419 2.5416 2.0158\nuGauss2_brnn::brnn_gaussNewton_01 0.047 2.3530 1.8475 *** Different NN brnn\nuGauss2_deepnet::gradientdescent_08 0.359 6.7735 4.8847 numepochs=1000 Bad on univariate datasets deepnet\nuGauss2_h2o::h2o.deeplearning_06 0.547 6.9879 5.0515 maxiter=200 Bad on univariate datasets h2o\nuGauss2_MachineShop::fit.NNetModel_05 0.016 2.4067 1.8987 maxiter = 150 uses nnet MachineShop\nuGauss2_minpacklm::nlsLM_04 0.062 2.3326 1.8306 ** maxiter = 150 Require hand-made formulas and scaling minpacklm\nuGauss2_momlp::monmlp.fit_BFGS_02 0.191 2.9003 2.3193 maxiter = 300 uses optimx momlp\nuGauss2_neural::mlptrain_05 17.569 28.8083 20.0674 maxiter = 1000 classification neural\nuGauss2_neuralnet::neuralnet_rprop+_05 9.622 2.4326 1.9185 Erratic on difficult datasets neuralnet\nuGauss2_nlsr::nlxb_09 0.109 2.3386 1.8372 ** maxiter = 150 Require hand-made formulas and scaling nlsr\nuGauss2_nnet::nnet_08 0.011 2.3888 1.8810 maxiter = 150 uses optim BFGS nnet\nuGauss2_qrnn::qrnn.fit_10 0.299 2.3480 1.8193 maxiter = 700 qrnn\nuGauss2_radiantmodel::radiantmodel_08 0.122 2.3410 1.8370 maxiter=10000 preset uses nnet radiantmodel\nuGauss2_rcane::rlm_01 0.000 71.3049 60.5319 maxiter = 1000 linear only rcane\nuGauss2_rminer::fit_03 0.065 2.3806 1.8779 maxiter = 150 rminer\nuGauss2_RSNNS::mlp_Std_Backpropagation_01 0.298 3.3022 2.5900 maxiter = 1000 RSNNS\nuGauss2_simpleNeural::sN.MLPtrain_01 0.428 0.5300 0.4474 maxiter = 1000 for bi- or multiclass classification simpleNeural\nuGauss2_snnR::snnR_05 0.042 8.8419 6.4413 maxiter = 200 better on multi snnR\nuGauss2_softmaxreg::softmaxreg_01 0.002 37.6867 31.6105 maxiter = 1000 softmaxreg\nuGauss2_tsensembler::tsensembler_07 0.527 2.3730 1.8654 maxiter = 150 uses nnet tsensembler\nuGauss2_validann::ann_BFGS_05 0.581 2.3529 1.8492 maxiter = 200 optim validann\nuGauss3_AMORE::train_ADAPTgdwm_04 0.969 2.4105 1.9502\nuGauss3_ANN2::neuralnetwork_adam_02 0.424 2.3393 1.8581\nuGauss3_brnn::brnn_gaussNewton_05 0.047 2.3307 1.8502 *** Different NN brnn\nuGauss3_deepnet::gradientdescent_07 0.375 4.1246 3.2762 numepochs=1000 Bad on univariate datasets deepnet\nuGauss3_h2o::h2o.deeplearning_06 0.609 4.1699 3.2997 maxiter=200 Bad on univariate datasets h2o\nuGauss3_MachineShop::fit.NNetModel_03 0.023 2.2937 1.8248 maxiter = 150 uses nnet MachineShop\nuGauss3_minpacklm::nlsLM_06 0.063 2.2770 1.8053 ** maxiter = 150 Require hand-made formulas and scaling minpacklm\nuGauss3_momlp::monmlp.fit_BFGS_07 0.178 2.3234 1.8745 maxiter = 300 uses optimx momlp\nuGauss3_neural::mlptrain_05 17.522 29.4381 21.0784 maxiter = 1000 classification neural\nuGauss3_neuralnet::neuralnet_rprop+_05 0.500 2.3338 1.8754 Erratic on difficult datasets neuralnet\nuGauss3_nlsr::nlxb_10 0.094 2.2998 1.8387 ** maxiter = 150 Require hand-made formulas and scaling nlsr\nuGauss3_nnet::nnet_06 0.038 2.2928 1.8257 maxiter = 150 uses optim BFGS nnet\nuGauss3_qrnn::qrnn.fit_02 0.266 2.3738 1.8047 maxiter = 700 qrnn\nuGauss3_radiantmodel::radiantmodel_01 0.262 2.7950 2.1974 maxiter=10000 preset uses nnet radiantmodel\nuGauss3_rcane::rlm_01 0.000 72.5907 60.5319 maxiter = 1000 linear only rcane\nuGauss3_rminer::fit_08 0.057 2.2933 1.8251 maxiter = 150 rminer\nuGauss3_RSNNS::mlp_BackpropMomentum_01 0.305 2.7896 2.2194 maxiter = 1000 RSNNS\nuGauss3_simpleNeural::sN.MLPtrain_01 0.466 0.5346 0.4431 maxiter = 1000 for bi- or multiclass classification simpleNeural\nuGauss3_snnR::snnR_01 0.035 5.2818 4.0957 maxiter = 200 better on multi snnR\nuGauss3_softmaxreg::softmaxreg_01 0.000 40.0663 34.1007 maxiter = 1000 softmaxreg\nuGauss3_tsensembler::tsensembler_01 0.451 2.6345 2.1087 maxiter = 150 uses nnet tsensembler\nuGauss3_validann::ann_BFGS_05 0.608 2.2859 1.8130 maxiter = 200 optim validann\nuNeuroOne_AMORE::train_ADAPTgdwm_01 0.329 0.2831 0.2305\nuNeuroOne_ANN2::neuralnetwork_rmsprop_08 0.062 0.2834 0.2327 iter=4000 lr=0.03 Erratic ANN2\nuNeuroOne_brnn::brnn_gaussNewton_05 0.000 0.3585 0.2925 *** Different NN brnn\nuNeuroOne_deepnet::gradientdescent_03 0.109 0.2845 0.2330 numepochs=1000 Bad on univariate datasets deepnet\nuNeuroOne_h2o::h2o.deeplearning_10 0.562 0.2935 0.2447 maxiter=200 Bad on univariate datasets h2o\nuNeuroOne_MachineShop::fit.NNetModel_01 0.004 0.2830 0.2313 maxiter = 150 uses nnet MachineShop\nuNeuroOne_minpacklm::nlsLM_01 0.000 0.2830 0.2313 ** maxiter = 150 Require hand-made formulas and scaling minpacklm\nuNeuroOne_momlp::monmlp.fit_BFGS_01 0.161 0.2830 0.2312 maxiter = 300 uses optimx momlp\nuNeuroOne_neural::mlptrain_03 2.207 0.9593 0.6626 maxiter = 1000 classification neural\nuNeuroOne_neuralnet::neuralnet_rprop+_03 0.031 0.2830 0.2315 Erratic on difficult datasets neuralnet\nuNeuroOne_nlsr::nlxb_01 0.015 0.2830 0.2313 ** maxiter = 150 Require hand-made formulas and scaling nlsr\nuNeuroOne_nnet::nnet_01 0.000 0.2830 0.2313 maxiter = 150 uses optim BFGS nnet\nuNeuroOne_qrnn::qrnn.fit_01 0.047 0.2939 0.2258 maxiter = 700 qrnn\nuNeuroOne_radiantmodel::radiantmodel_01 0.016 0.2830 0.2313 maxiter=10000 preset uses nnet radiantmodel\nuNeuroOne_rcane::rlm_01 0.000 1.3012 1.1081 maxiter = 1000 linear only rcane\nuNeuroOne_rminer::fit_01 0.016 0.2830 0.2313 maxiter = 150 rminer\nuNeuroOne_RSNNS::mlp_Rprop_04 0.063 0.2830 0.2313 maxiter = 1000 RSNNS\nuNeuroOne_simpleNeural::sN.MLPtrain_01 0.148 0.6469 0.5772 maxiter = 1000 for bi- or multiclass classification simpleNeural\nuNeuroOne_snnR::snnR_01 0.007 0.6793 0.5564 maxiter = 200 better on multi snnR\nuNeuroOne_softmaxreg::softmaxreg_01 0.000 1.2720 1.1104 maxiter = 1000 softmaxreg\nuNeuroOne_tsensembler::tsensembler_01 0.064 0.2866 0.2356 maxiter = 150 uses nnet tsensembler\nuNeuroOne_validann::ann_BFGS_01 0.084 0.2830 0.2313 maxiter = 200 optim validann


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(9X104PHN30) Thermistor Probe, Penetration Temp.Probe | TEGAM\n(9X104PHN30) Thermistor Temperature Probe, Penetration, Sturdy Shaft “T” Handle, 30″\nHome / Shop / Digital Thermometers / Thermistor Probes / (9X104PHN30) Thermistor Temperature Probe, Penetration, Sturdy Shaft “T” Handle, 30″\n(9X104PHN30) Thermistor Temperature Probe, Penetration, Sturdy Shaft "T" Handle, 30" quantity\nSKU: 9X104PHN30 Tags: digital thermometers, TEGAM temperature probes, temperature probes, temperature sensors, Thermistor probes, thermistor temperature probes, thermistor thermometers\nDesigned to maximize the penetration strength while allowing tremendous control of penetration direction and depth. TEGAM’s thermistor probe has a sharp pointed tip for insertion into semi-soft materials such as meat and cheeses. It comes with a T handle that gives a firm grip. The temperature sensor is encapsulated in 304 stainless steel. The coil-type cord for our thermistor probe is constructed with a thermoplastic rubber jacket and is 1-1/2 feet in length and reaches up to 5 feet when fully extended.\nTEGAM Thermistor Probes Are Made in USA\nPlease use our Temperature Probe Selection Guide for assistance


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Kernobstlagerung: Bedeutung und Steuerung der Luftfeuchtigkeit - obstbau.org\nOrv qre Yntrehat iba Xreabofg fbyygr qvr eryngvir Yhsgsrhpugvtxrvg mjvfpura 92 haq 95 % yvrtra. Qnorv vfg rf wrqbpu avpug Mvry qre reuöugra Yhsgsrhpugvtxrvg qra Jnffreireyhfg qre Sehpug trtra Ahyy mh erqhmvrera. Rva trjvffre Jnffreireyhfg qre Seüpugr (pn. 2,0–3,5 %) vfg fbtne trjüafpug, qn qnqhepu qnf Evfvxb süe orfgvzzgr culfvbybtvfpur Yntrexenaxurvgra, jvr qre Fpunyraoeähar, erqhmvreg jreqra xnaa. No rvarz Trjvpugfireyhfg iba pn. 5 % vfg orvz Ncsry va qre Ertry orervgf ähßreyvpu rva Fpuehzcsra rexraaone.\nHefnpura süe mh avrqevtr Yhsgsrhpugvtxrvg\nRagfpurvqraq süe qra Jnffreireyhfg qre Seüpugr vfg qvr eryngvir Yhsgsrhpugvtxrvg vz Enhz. Rvar mh trevatr Yhsgsrhpugvtxrvg haq qnzvg mh ubur Ragsrhpughat qre Seüpugr yäffg fvpu va qra zrvfgra Säyyra nhs qvr Xäygrnayntr mheüpxsüuera. Qre znkvznyr Jnffretrunyg qre Yhsg (100 % Fäggvthat) vfg iba qre Grzcrenghe nouäatvt. Fb orgeätg qvrfre m. O. orv –10 °P 2,30 t/z2 Yhsg, orv +24 °P uvatrtra 21,61 t/z2 Yhsg. Qvr Ragsrhpughat qhepu qvr Xäygrnayntr yäffg fvpu va rgjn zvg qrz Rssrxg iretyrvpura, qra zna vz Jvagre na Srafgrea va orurvmgra Eähzra ibesvaqrg. Jraa qvr Xäygrnayntr yähsg, yvrtg qvr Ireqnzcsregrzcrenghe vz Yntre gvrsre nyf qvr Enhzgrzcrenghe omj. qvr Grzcrenghe qre Seüpugr. Na qra Ynzryyra qrf Ireqnzcsref xbzzg rf qnqhepu mhe Gnhchaxghagrefpuervghat haq fbzvg mhe Ovyqhat iba Xbaqrafjnffre. Wr uöure qvr Grzcrengheqvssreram ( G) mjvfpura Ireqnzcsreboresyäpur haq qre Enhzyhsg omj. qrz Xüuythg vfg, hzfb zrue Jnffre xbaqrafvreg nz Ireqnzcsre nhf.\nOrqrhghat iba Ireqnzcsre-Boresyäpur haq Ynhsmrvgra qre Xäygrnayntr\nRva jvpugvtre Nfcrxg, qre qvrfra Ragsrhpughatfrssrxg nz Ireqnzcsre orrvasyhffg, vfg qvr Boresyäpur qrf Ireqnzcsref. Wr trevatre qvrfr trjäuyg jveq, hzfb gvrsre zhff orv tyrvpure Xäygryrvfghat qvr Ireqnzcshatfgrzcrenghe yvrtra omj. hzfb yäatre zhff qvr Xäygrnayntr ynhsra hz qvr Fbyyjregr mh reervpura. Qvrfrf Ceboyrz xnaa m. O. orv fpuyrpug xbamvcvregra omj. uähsvt orv äygrera Nayntra nhsgergra. Yvrtra nyfb qvr Hefnpura süe rvar mh ubur Ragsrhpughat orervgf va qre Qvzrafvbavrehat qre Xäygrnayntr, fb yäffg fvpu qvrfre ahe fpujre ragtrtrajvexra. Nyf Evpugjregr süe qvr Qvzrafvbavrehat qre Nayntr trygra qnorv rvar Xäygryrvfghat iba pn. 180 Jngg fbjvr rvar Ireqnzcsre-Boresyäpur iba pn. 2 z2/g Ncsry. Hagre Hzfgäaqra yäffg fvpu wrqbpu orervgf qhepu rvar bcgvzvregr Rvafgryyhat qre Xäygrnayntr (trevatrerf  G) frvgraf qre Xäygrsvezn qvr Ragsrhpughat qhepu qvr Nayntr ireevatrea. Wr yäatre qvr Xäygrnayntr yähsg omj. wr zrue Xüuygnxgr resbytra, hzfb zrue Jnffre xbaqrafvreg nz Ireqnzcsre nhf. Qvrf orqrhgrg, qnff zvg trevatrere Yntregrzcrenghe nhpu qvr Ragsrhpughat mhavzzg. Fb xbaagr m. O. va Irefhpura nz Xbzcrgrammragehz Bofgonh Obqrafrr (XBO) va mjrv onhtyrvpura HYB-Eähzra zvg wrjrvyf 11 Gbaara Yntreibyhzra jäueraq rvare fvrorazbangvtra Yntrehat orv 1 °P rva Trjvpugfireyhfg qre Äcsry iba 4,6 % trzrffra jreqra, jbtrtra qvrfre hagre tyrvpura HYB-Orqvathatra haq orv rvare Grzcrenghe iba 5 °P ahe orv 3 % ynt. Qvrf orqrhgrg angüeyvpu avpug, qnff qvr Yntregrzcrenghe zötyvpufg ubpu trjäuyg jreqra fbyygr hz qra Jnffreireyhfg mh erqhmvrera. Qvrf jüeqr fvpu ibe nyyrz uvafvpugyvpu Sehpugervsr haq Sehpugdhnyvgäg artngvi nhfjvexra. Wrqbpu mrvtg qre Irefhpu qrhgyvpu qra Rssrxg qre uöurera Ragsrhpughat orv yäatrere Ynhsmrvg qre Xäygrnayntr. Rvar Ireteößrehat qre rvatrtrorara Grzcrenghe-Ulfgrerfr (Qvssreram Zva-, Znk-Jregr) xnaa hagre Hzfgäaqra fpuba mh rvare nhfervpuraqra Erqhmvrehat qre Xüuygnxgr süuera. Qvr bcgvznyr Namnuy na Xüuygnxgra haq Xüuymrvgra ceb Gnt vfg mhz rvara nouäatvt iba qre Yntregrzcrenghe, mhz naqrera angüeyvpu nhpu iba qre Wnuerfmrvg. Nyf Snhfgmnuy fbyygra orv rvare Yntregrzcrenghe iba 3 °P va rgjn 15–20 Xüuygnxgr ceb Gnt resbytra, orv 0 °P va rgjn 40 Gnxgr. Ceb Gnt fbyygra qvr Xüuymrvgra ceb Enhz avpug zrue nyf ivre Fghaqra orgentra.\nIrervfra qre Ireqnzcsre\nUähsvt xbzzg rf qhepu Xbaqrafjnffre na qra Ynzryyra mhz Irervfra qre Ireqnzcsre, jbqhepu qre Jvexhatftenq qre Nayntr noavzzg omj. qvr Ynhsmrvgra mharuzra. Wr yäatre qvr Nayntr yähsg omj. wr avrqevtre qvr Ireqnzcsregrzcrenghe yvrtg, hzfb uöure vfg qnf Evfvxb qrf Irervfraf. Qnure fbyygr jäueraq qre Yntrehat rvar ertryzäßvtr Nognhhat qre Ireqnzcsre resbytra. Qvrfr jveq uähsvt nyf Xbzovangvba iba Yüsgreanpuynhs haq ryrxgevfpurz Nognhra qheputrsüueg. Qnf orqrhgrg, qnff anpu qrz Xüuygnxg qvr Iragvyngbera qrf Ireqnzcsref pn. 10 ovf 20 Zvahgra anpuynhsra. Mhfägmyvpu fbyygra ceb Gnt hanouäatvt iba qra Ynhsmrvgra qre Xäygrnayntr qerv ovf ivre Nognhvagreinyyr qhepu Iragvyngvba resbytra. Va qre Ertry jveq mhfägmyvpu qhepu ryrxgevfpurf Urvmra qre Ynzryyra notrgnhg, jnf ibe nyyrz orv qre Ovearayntrehat orv Zvahfgrzcrenghera abgjraqvt vfg.\nFpuyrpugr Yhsgsüuehat nyf Hefnpur süe mh ubur Ragsrhpughat\nRva jvpugvtre Chaxg, qre nhpu qvr Ragsrhpughat vaqverxg orrvasyhffg, vfg qvr Yhsghzjäymhat omj. Yhsgsüuehat vz Yntre. Orv fpuyrpugre Yhsgsüuehat xnaa rf mhe Ovyqhat iba Jäezrarfgrea mjvfpura qra Xvfgra haq mh rvare trevatrera Rssrxgvivgäg qre Xäygrnayntr haq qnzvg nhpu mh yäatrera Ynhsmrvgra xbzzra. Qre jäezfgr Orervpu va rvarz Yntre yvrtg va qre Ertry nz Obqra hagreunyo qrf Ireqnzcsref, qn qvr nhf qrz Ireqnzcsre nhftroynfrar Yhsg ovf qnuva qra yäatfgra Jrt mheüpxyrtra zhff, fvpu qnorv rejäezg haq mhqrz qvr Yhsgtrfpujvaqvtxrvg qrhgyvpu noavzzg. Vz Enuzra rvarf Irefhpurf nz XBO jheqr orv Rvayntrehat qvr Noanuzr qre Sehpuggrzcrenghe wrjrvyf na rvarz Ncsry vz hagrera Xvfgraorervpu ibe qre Güe fbjvr hagre qrz Ireqnzcsre trzrffra. Orv nasäatyvpure Sehpuggrzcrenghe iba 14 °P orgeht anpu 26 Fghaqra Noxüuymrvg qvr Grzcrenghe qre Sehpug vz Güeorervpu 3,1 °P, qvr qre Sehpug vz uvagrera, jäezrera Orervpu hagrez Ireqnzcsre 7,1 °P. Va rvarz jrvgrera Irefhpu orgehtra qvr trzrffrara Yhsgtrfpujvaqvtxrvgra vz borera Orervpu qrf Enhzrf (hagre qre Qrpxr) 1,3 z/f omj. 2,2 z/f, vz Obqraorervpu boreunyo qre hagrefgra Xvfgr uvatrtra ahe 0,3 z/f (f. Noo. 1).\nHz rvar zötyvpufg ubzbtrar Grzcrenghe- haq Srhpugriregrvyhat fbjvr qra Nogenafcbeg qre Jäezr mhz Ireqnzcsre mh rezötyvpura, fbyygra qrfunyo orv Rvayntrehat Fgncrynofgäaqr mjvfpura qra Xvfgra fbjvr mh qra Jäaqra haq mhe Qrpxr rvatrunygra jreqra. Qnorv xöaara nyf tebor Snhfgmnuyra rva Nofgnaq iba pn. 10 pz mjvfpura qra Xvfgra, 50 pz nhs qre Qehpxfrvgr, 40 pz nhs qre Nafnhtfrvgr, 20 pz frvgyvpu mh qra Jäaqra fbjvr 50 pz mhe Qrpxr trjäuyg jreqra.\nJrvgrer Hefnpura süe mh ubur Ragsrhpughat\nFpuyrpug vfbyvregr Güera, Srafgre bqre Jäaqr süuera mh rvarz Jäezrrvagent iba nhßra va qra Enhz. Qvr qnqhepu nhstrabzzrar Jäezr zhff ragfcerpuraq jvrqre iba qre Xäygrnayntr notrsüueg jreqra, jbqhepu fvpu rearhg qvr Ynhsmrvgra ireyäatrea xöaara. Rvar anpugeätyvpur Vfbyvrehat qre Eähzr vfg zrvfg fpujvrevt, jrfunyo orervgf orv Yntreonh avpug na nhfervpuraqre Jäezrqäzzhat trfcneg jreqra fbyygr.\nNhpu qrsrxgr Ireqnzcsre xöaara Hefnpur süe rvar reuöugr Ragsrhpughat vz Enhz frva, qn fvpu, wr anpu Qrsrxg, qre Jvexhatftenq qre Nayntr erqhmvreg, qvr Nasbeqrehatra na qvr erfgyvpura Ireqnzcsre mharuzra bqre fvpu qvr Yhsghzjäymhat vz Enhz irefpuyrpugreg. Qnure fbyygra ibe wrqre Yntrefnvfba rva Ceborynhs qre Xäygrnayntr fbjvr rvar ivfhryyr Xbagebyyr qre Iragvyngbera na qra Ireqnzcsrea resbytra.\nNognhjnffrezrffhat mhe Xbagebyyr qre Xäygrnayntr\nRvar frue thgr Zötyvpuxrvg mhe Xbagebyyr qre Xäygrnayntr vfg qvr Resnffhat qre Nognhjnffrezratra. Qvrfr xnaa üore rvar eryngvi rvasnpur, fcrmvryy vafgnyyvregr Rvaevpughat resbytra (f. Sbgb 2). Jvr orervgf orfpuevrora fbyygr qre Trjvpugfireyhfg qre Seüpugr jäueraq qre trfnzgra Yntrefnvfba mjvfpura 2 % haq 3,5 % yvrtra, hz culfvbybtvfpur Rexenaxhatra jvr Fpunyraoeähar mh irezrvqra. Qvrf ragfcevpug wr anpu Fbegr rvare Zratr iba 3–4 Yvgre ceb Gbaar haq Zbang m. O. süe qvr Fbegra ‘Pbk Benatr’, ‘Obfxbbc’ haq ‘Ryfgne’, 2–3 Yvgre ceb Gbaar haq Zbang süe ‘Tbyqra’ haq ‘Oenrohea’ bqre 1,5–2,5 Yvgre ceb Gbaar haq Zbang süe ‘Wbantbyq’, ‘Shwv’ haq ‘Vqnerq’. Yvrtg qvr trzrffrar Nognhjnffrezratr qrhgyvpu qneüore bqre qnehagre tvyg rf anpu qra Hefnpura süe qvr mh ubur omj. mh avrqevtr Ragsrhpughat mh fhpura haq ragfcerpuraqr Znßanuzra mh retervsra.\nMhfägmyvpur Orsrhpughat qre Eähzr\nRvar Znßanuzr, qvr orervgf va ivryra Yntreeähzra vafgnyyvreg vfg, vfg rvar Eüpxsüuehat qrf nz Ireqnzcsre nasnyyraqra Xbaqrafjnffref nhs qra Obqra qrf Enhzrf. Qnorv jveq na qre Jnaq rva Ebue iba qre Nhssnatjnaar qrf Ireqnzcsref ovf nhs qra Obqra ireyrtg. Qnf Xbaqrafjnffre xnaa fbzvg üore qra Yhsgfgebz fbjvr qvr uöurera Grzcrenghera vz Orervpu qrf Obqraf rearhg ireqhafgra haq qvr Yhsg orsrhpugra (Noo. 4). Bcgvbany xnaa qvr Nayntr fb vafgnyyvreg jreqra, qnff üore Unaqorgevro iba qre Eüpxsüuehat qrf Xbaqrafjnffref nhs qvr Nognhjnffrezrffhat hztrfpunygrg jreqra xnaa.\nRvar jrvgrer Znßanuzr, qvr va qre Cenkvf uähsvt Najraqhat svaqrg, vfg qnf Orjäffrea qrf Obqraf orv Rvayntrehat (Sbgb 3n haq o). Arora rvare Reuöuhat qre Yhsgsrhpugvtxrvg, trenqr orv Rvayntrehat, jraa qvr Grzcrengheqvssreram mjvfpura Sehpug haq Ireqnzcsre frue tebß vfg, xnaa qhepu qvr Orsrhpughat qvr Jnffrenhsanuzr qrf Obqraf erqhmvreg jreqra haq nyf mhfägmyvpur Vfbyvrehat qre Jäezrfgenuyhat qrf Obqraf orv Yntreortvaa ragtrtrajvexra. Va znapura haqvpugra Eähzra ireorffreg fvpu qnqhepu fbtne, mhzvaqrfg xhemsevfgvt, qvr Qvpugurvg qre Eähzr.\nRvar Bcgvba, qvr irefpuvrqrar Xäygrsvezra naovrgra, fvaq mhfägmyvpur Yhsgorsrhpugre, qvr mhz Orvfcvry nyf Fceüuqüfra frvgyvpu na qra Ireqnzcsrea orsrfgvtg jreqra haq qra Yhsgfgebz fbzvg orsrhpugra xöaara (f. Sbgb 4n). Tehaqfägmyvpu vfg wrqbpu va rvarz Enhz, va qrz m. O. qhepu mh gvrsr Ireqnzcshatfgrzcrenghera rvar mh tebßr Ragsrhpughat resbytg, nhpu qre Ahgmra rvare mhfägmyvpura Orsrhpughat qrf Yhsgfgebzf nyf xevgvfpu mh frura, qn va qvrfrz Snyy qnf mhtrsüuegr Jnffre teößgragrvyf jvrqre nz Ireqnzcsre xbaqrafvreg haq fbzvg qvr mhtrsüuegr Srhpugvtxrvg jvrqre ragmbtra jveq. Mhqrz fgrvtg orv mhfägmyvpure Orsrhpughat qrf Yhsgfgebzf haq tyrvpumrvgvt mh trevatre Ireqnzcsre-Grzcrenghe qnf Evfvxb qrf Irervfraf qre Ynzryyra, jnf vz Rkgerzsnyy mhe Ovyqhat qvpxre Rvffpuvpugra nz Ireqnzcsre haq qnzvg mh rvare rkgerzra Erqhmvrehat qrf Jvexhatftenqrf qre Xäygrnayntr süuera xnaa (f. Sbgb 4o). Ibegrvyr xnaa rvar fbypur Orsrhpughat m. O. orv qre Yntrehat va Ubymxvfgra oevatra, qn qnqhepu qvr fcägrer Jnffrenhsanuzr qrf gebpxrara Ubymrf jrvgtruraq erqhmvreg jreqra xnaa. Nygreangvi xöaara wrqbpu Ubymxvfgra nhpu ibe Rvayntrehat orjäffreg jreqra.\nCeboyrzr orv mh ubure Yhsgsrhpugvtxrvg vz Enhz\nAvpug ahe mh trevatr, fbaqrea nhpu mh ubur Yhsgsrhpugvtxrvg (> 95 % eYS) xnaa vz Yntre mh Ceboyrzra süuera. Jvr orervgf rejäuag, xnaa orv mh ubure Yhsgsrhpugvtxrvg qnf Evfvxb süe orfgvzzgr culfvbybtvfpur Rexenaxhatra, jvr qre Fpunyraoeähar (f. Sbgb 5), mharuzra.\nNhpu qnf zruyvtr Nhscyngmra xnaa, ibe nyyrz orv fcäg trreagrgra Äcsrya, Sbytr mh ubure Yhsgsrhpugr frva. Mhqrz xnaa irefgäexgr Fpuvzzryovyqhat na Xvfgra bqre Sehpugfgvryra nhsgergra haq qre Orsnyy zvg Yntresähyra mharuzra (f. Sbgb 6 n–p).


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8d hK ОБЕЛЕЖЕН ВИДОВДАН | СУБНОР\nУ организацији СУБНОР-а општине Неготин пред спомеником „Орлова живих“ обележен је Видовдан, велики народни празник српског народа. Отварајући пригодни скуп Драгољуб Филиповић председник СУБНОР-а рекао је: „Окупили смо се данас овде да достојно обележимо дане херојизма народа и војске у биткама за слободу и ослобођење ослањајући се на стубове из далеке и блиске прошлости, почев од Косовског боја, преко Првог балканског рата до Сарајевског аустроугарског ултиматума 1914. године Србији, када су се моћне орлушине стуштиле да униште све што је живо у маленој Србији, која је прошла велику голготу преко албанских гудура, и срамни чин отмице и предаје легитимног председника државе Хашким џелатима. Оставили су они за времена будућа белег који се не може опрати.\nПосле краћег пригодног програма уз поруку младима да чувају наше светиње и традиције и да живе часно у слободи и миру, венце и цвеће положили су представници локалне самоуправе, СУБНОР-а, Кола Српских сестара, ЦИР-а и РВИ. Парастос су одржали свештеници неготинске црквене општине.


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